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recognition</title> <meta name="description" content="Search results for: molecular recognition"> <meta name="keywords" content="molecular recognition"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="molecular recognition"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3753</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: molecular recognition</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3753</span> An Insight into the Conformational Dynamics of Glycan through Molecular Dynamics Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Veluraja">K. Veluraja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glycan of glycolipids and glycoproteins is playing a significant role in living systems particularly in molecular recognition processes. Molecular recognition processes are attributed to their occurrence on the surface of the cell, sequential arrangement and type of sugar molecules present in the oligosaccharide structure and glyosidic linkage diversity (glycoinformatics) and conformational diversity (glycoconformatics). Molecular Dynamics Simulation study is a theoretical-cum-computational tool successfully utilized to establish glycoconformatics of glycan. The study on various oligosaccharides of glycan clearly indicates that oligosaccharides do exist in multiple conformational states and these conformational states arise due to the flexibility associated with a glycosidic torsional angle (φ,ψ) . As an example: a single disaccharide structure NeuNacα(2-3) Gal exists in three different conformational states due to the differences in the preferential value of glycosidic torsional angles (φ,ψ). Hence establishing three dimensional structural and conformational models for glycan (cartesian coordinates of every individual atoms of an oligosaccharide structure in a preferred conformation) is quite crucial to understand various molecular recognition processes such as glycan-toxin interaction and glycan-virus interaction. The gycoconformatics models obtained for various glycan through Molecular Dynamics Simulation stored in our 3DSDSCAR (3DSDSCAR.ORG) a public domain database and its utility value in understanding the molecular recognition processes and in drug design venture will be discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=glycan" title="glycan">glycan</a>, <a href="https://publications.waset.org/abstracts/search?q=glycoconformatics" title=" glycoconformatics"> glycoconformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20dynamics%20simulation" title=" molecular dynamics simulation"> molecular dynamics simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=oligosaccharide" title=" oligosaccharide"> oligosaccharide</a> </p> <a href="https://publications.waset.org/abstracts/96790/an-insight-into-the-conformational-dynamics-of-glycan-through-molecular-dynamics-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3752</span> Handwriting Recognition of Gurmukhi Script: A Survey of Online and Offline Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravneet%20Kaur">Ravneet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character recognition is a very interesting area of pattern recognition. From past few decades, an intensive research on character recognition for Roman, Chinese, and Japanese and Indian scripts have been reported. In this paper, a review of Handwritten Character Recognition work on Indian Script Gurmukhi is being highlighted. Most of the published papers were summarized, various methodologies were analysed and their results are reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmukhi%20character%20recognition" title="Gurmukhi character recognition">Gurmukhi character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=online" title=" online"> online</a>, <a href="https://publications.waset.org/abstracts/search?q=offline" title=" offline"> offline</a>, <a href="https://publications.waset.org/abstracts/search?q=HCR%20survey" title=" HCR survey"> HCR survey</a> </p> <a href="https://publications.waset.org/abstracts/46337/handwriting-recognition-of-gurmukhi-script-a-survey-of-online-and-offline-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3751</span> OCR/ICR Text Recognition Using ABBYY FineReader as an Example Text</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20R.%20Bagirzade">A. R. Bagirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sh.%20Najafova"> A. Sh. Najafova</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Yessirkepova"> S. M. Yessirkepova</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20S.%20Albert"> E. S. Albert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABBYY%20FineReader%20system" title="ABBYY FineReader system">ABBYY FineReader system</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20symbol%20recognition" title=" algorithm symbol recognition"> algorithm symbol recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR%2FICR%20techniques" title=" OCR/ICR techniques"> OCR/ICR techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20technologies" title=" recognition technologies"> recognition technologies</a> </p> <a href="https://publications.waset.org/abstracts/130255/ocricr-text-recognition-using-abbyy-finereader-as-an-example-text" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130255.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3750</span> Host-Assisted Delivery of a Model Drug to Genomic DNA: Key Information From Ultrafast Spectroscopy and in Silico Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ria%20Ghosh">Ria Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Soumendra%20Singh"> Soumendra Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Dipanjan%20Mukherjee"> Dipanjan Mukherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Susmita%20Mondal"> Susmita Mondal</a>, <a href="https://publications.waset.org/abstracts/search?q=Monojit%20Das"> Monojit Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Uttam%20Pal"> Uttam Pal</a>, <a href="https://publications.waset.org/abstracts/search?q=Aniruddha%20Adhikari"> Aniruddha Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Aman%20Bhushan"> Aman Bhushan</a>, <a href="https://publications.waset.org/abstracts/search?q=Surajit%20Bose"> Surajit Bose</a>, <a href="https://publications.waset.org/abstracts/search?q=Siddharth%20Sankar%20Bhattacharyya"> Siddharth Sankar Bhattacharyya</a>, <a href="https://publications.waset.org/abstracts/search?q=Debasish%20Pal"> Debasish Pal</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanusri%20Saha-Dasgupta"> Tanusri Saha-Dasgupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Maitree%20Bhattacharyya"> Maitree Bhattacharyya</a>, <a href="https://publications.waset.org/abstracts/search?q=Debasis%20Bhattacharyya"> Debasis Bhattacharyya</a>, <a href="https://publications.waset.org/abstracts/search?q=Asim%20Kumar%20Mallick"> Asim Kumar Mallick</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjan%20Das"> Ranjan Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Samir%20Kumar%20Pal"> Samir Kumar Pal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drug delivery to a target without adverse effects is one of the major criteria for clinical use. Herein, we have made an attempt to explore the delivery efficacy of SDS surfactant in a monomer and micellar stage during the delivery of the model drug, Toluidine Blue (TB) from the micellar cavity to DNA. Molecular recognition of pre-micellar SDS encapsulated TB with DNA occurs at a rate constant of k1 ~652 s 1. However, no significant release of encapsulated TB at micellar concentration was observed within the experimental time frame. This originated from the higher binding affinity of TB towards the nano-cavity of SDS at micellar concentration which does not allow the delivery of TB from the nano-cavity of SDS micelles to DNA. Thus, molecular recognition controls the extent of DNA recognition by TB which in turn modulates the rate of delivery of TB from SDS in a concentration-dependent manner. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA" title="DNA">DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20delivery" title=" drug delivery"> drug delivery</a>, <a href="https://publications.waset.org/abstracts/search?q=micelle" title=" micelle"> micelle</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-micelle" title=" pre-micelle"> pre-micelle</a>, <a href="https://publications.waset.org/abstracts/search?q=SDS" title=" SDS"> SDS</a>, <a href="https://publications.waset.org/abstracts/search?q=toluidine%20blue" title=" toluidine blue"> toluidine blue</a> </p> <a href="https://publications.waset.org/abstracts/154090/host-assisted-delivery-of-a-model-drug-to-genomic-dna-key-information-from-ultrafast-spectroscopy-and-in-silico-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">113</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3749</span> An Improved OCR Algorithm on Appearance Recognition of Electronic Components Based on Self-adaptation of Multifont Template</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhu-Qing%20Jia">Zhu-Qing Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Lin"> Tao Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Tong%20Zhou"> Tong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The recognition method of Optical Character Recognition has been expensively utilized, while it is rare to be employed specifically in recognition of electronic components. This paper suggests a high-effective algorithm on appearance identification of integrated circuit components based on the existing methods of character recognition, and analyze the pros and cons. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20character%20recognition" title="optical character recognition">optical character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20page%20identification" title=" fuzzy page identification"> fuzzy page identification</a>, <a href="https://publications.waset.org/abstracts/search?q=mutual%20correlation%20matrix" title=" mutual correlation matrix"> mutual correlation matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20self-adaptation" title=" confidence self-adaptation"> confidence self-adaptation</a> </p> <a href="https://publications.waset.org/abstracts/14322/an-improved-ocr-algorithm-on-appearance-recognition-of-electronic-components-based-on-self-adaptation-of-multifont-template" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14322.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">540</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3748</span> Facial Recognition on the Basis of Facial Fragments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk">Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Bonilla%20Meza"> Sandra Bonilla Meza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild<em>) </em>face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=labeled%20faces%20in%20the%20wild%20%28LFW%29%20database" title=" labeled faces in the wild (LFW) database"> labeled faces in the wild (LFW) database</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20local%20descriptor%20%28RLD%29" title=" random local descriptor (RLD)"> random local descriptor (RLD)</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20features" title=" random features"> random features</a> </p> <a href="https://publications.waset.org/abstracts/50117/facial-recognition-on-the-basis-of-facial-fragments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50117.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3747</span> DBN-Based Face Recognition System Using Light Field</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bing%20Gu">Bing Gu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Abstract—Most of Conventional facial recognition systems are based on image features, such as LBP, SIFT. Recently some DBN-based 2D facial recognition systems have been proposed. However, we find there are few DBN-based 3D facial recognition system and relative researches. 3D facial images include all the individual biometric information. We can use these information to build more accurate features, So we present our DBN-based face recognition system using Light Field. We can see Light Field as another presentation of 3D image, and Light Field Camera show us a way to receive a Light Field. We use the commercially available Light Field Camera to act as the collector of our face recognition system, and the system receive a state-of-art performance as convenient as conventional 2D face recognition system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBN" title="DBN">DBN</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20field" title=" light field"> light field</a>, <a href="https://publications.waset.org/abstracts/search?q=Lytro" title=" Lytro"> Lytro</a> </p> <a href="https://publications.waset.org/abstracts/10821/dbn-based-face-recognition-system-using-light-field" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10821.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3746</span> Molecular Interactions Driving RNA Binding to hnRNPA1 Implicated in Neurodegeneration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sakina%20Fatima">Sakina Fatima</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph-Patrick%20W.%20E.%20Clarke"> Joseph-Patrick W. E. Clarke</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20A.%20Thibault"> Patricia A. Thibault</a>, <a href="https://publications.waset.org/abstracts/search?q=Subha%20Kalyaanamoorthy"> Subha Kalyaanamoorthy</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Levin"> Michael Levin</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindhan%20Ganesan"> Aravindhan Ganesan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heteronuclear ribonucleoprotein (hnRNPA1 or A1) is associated with the pathology of different diseases, including neurological disorders and cancers. In particular, the aggregation and dysfunction of A1 have been identified as a critical driver for neurodegeneration (NDG) in Multiple Sclerosis (MS). Structurally, A1 includes a low-complexity domain (LCD) and two RNA-recognition motifs (RRMs), and their interdomain coordination may play a crucial role in A1 aggregation. Previous studies propose that RNA-inhibitors or nucleoside analogs that bind to RRMs can potentially prevent A1 self-association. Therefore, molecular-level understanding of the structures, dynamics, and nucleotide interactions with A1 RRMs can be useful for developing therapeutics for NDG in MS. In this work, a combination of computational modelling and biochemical experiments were employed to analyze a set of RNA-A1 RRM complexes. Initially, the atomistic models of RNA-RRM complexes were constructed by modifying known crystal structures (e.g., PDBs: 4YOE and 5MPG), and through molecular docking calculations. The complexes were optimized using molecular dynamics simulations (200-400 ns), and their binding free energies were computed. The binding affinities of the selected complexes were validated using a thermal shift assay. Further, the most important molecular interactions that contributed to the overall stability of the RNA-A1 RRM complexes were deduced. The results highlight that adenine and guanine are the most suitable nucleotides for high-affinity binding with A1. These insights will be useful in the rational design of nucleotide-analogs for targeting A1 RRMs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hnRNPA1" title="hnRNPA1">hnRNPA1</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20docking" title=" molecular docking"> molecular docking</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20dynamics" title=" molecular dynamics"> molecular dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA-binding%20proteins" title=" RNA-binding proteins"> RNA-binding proteins</a> </p> <a href="https://publications.waset.org/abstracts/155246/molecular-interactions-driving-rna-binding-to-hnrnpa1-implicated-in-neurodegeneration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155246.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3745</span> Face Tracking and Recognition Using Deep Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Degale%20Desta">Degale Desta</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jian"> Cheng Jian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-RCNN" title=" fast-RCNN"> fast-RCNN</a> </p> <a href="https://publications.waset.org/abstracts/163134/face-tracking-and-recognition-using-deep-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163134.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3744</span> Comparing Emotion Recognition from Voice and Facial Data Using Time Invariant Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vesna%20Kirandziska">Vesna Kirandziska</a>, <a href="https://publications.waset.org/abstracts/search?q=Nevena%20Ackovska"> Nevena Ackovska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Madevska%20Bogdanova"> Ana Madevska Bogdanova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The problem of emotion recognition is a challenging problem. It is still an open problem from the aspect of both intelligent systems and psychology. In this paper, both voice features and facial features are used for building an emotion recognition system. A Support Vector Machine classifiers are built by using raw data from video recordings. In this paper, the results obtained for the emotion recognition are given, and a discussion about the validity and the expressiveness of different emotions is presented. A comparison between the classifiers build from facial data only, voice data only and from the combination of both data is made here. The need for a better combination of the information from facial expression and voice data is argued. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/42384/comparing-emotion-recognition-from-voice-and-facial-data-using-time-invariant-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42384.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3743</span> Possibilities, Challenges and the State of the Art of Automatic Speech Recognition in Air Traffic Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Van%20Nhan%20Nguyen">Van Nhan Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Harald%20Holone"> Harald Holone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Trafﬁc Control (ATC), such as air trafﬁc control simulation and training, monitoring live operators for with the aim of safety improvements, air trafﬁc controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this ﬁeld. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air trafﬁc control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as speciﬁc approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=asr" title=" asr"> asr</a>, <a href="https://publications.waset.org/abstracts/search?q=air%20traffic%20control" title=" air traffic control"> air traffic control</a>, <a href="https://publications.waset.org/abstracts/search?q=atc" title=" atc"> atc</a> </p> <a href="https://publications.waset.org/abstracts/31004/possibilities-challenges-and-the-state-of-the-art-of-automatic-speech-recognition-in-air-traffic-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31004.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3742</span> A Contribution to Human Activities Recognition Using Expert System Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with human activity recognition from sensor data. It is an active research area, and the main objective is to obtain a high recognition rate. In this work, a recognition system based on expert systems is proposed; the recognition is performed using the objects, object states, and gestures and taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions and the activity). The system recognizes complex activities after decomposing them into simple, easy-to-recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/171721/a-contribution-to-human-activities-recognition-using-expert-system-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171721.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3741</span> Switching to the Latin Alphabet in Kazakhstan: A Brief Overview of Character Recognition Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ainagul%20Yermekova">Ainagul Yermekova</a>, <a href="https://publications.waset.org/abstracts/search?q=Liudmila%20Goncharenko"> Liudmila Goncharenko</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Baghirzade"> Ali Baghirzade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Sybachin"> Sergey Sybachin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we address the problem of Kazakhstan's transition to the Latin alphabet. The transition process started in 2017 and is scheduled to be completed in 2025. In connection with these events, the problem of recognizing the characters of the new alphabet is raised. Well-known character recognition programs such as ABBYY FineReader, FormReader, MyScript Stylus did not recognize specific Kazakh letters that were used in Cyrillic. The author tries to give an assessment of the well-known method of character recognition that could be in demand as part of the country's transition to the Latin alphabet. Three methods of character recognition: template, structured, and feature-based, are considered through the algorithms of operation. At the end of the article, a general conclusion is made about the possibility of applying a certain method to a particular recognition process: for example, in the process of population census, recognition of typographic text in Latin, or recognition of photos of car numbers, store signs, etc. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title="text detection">text detection</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20method" title=" template method"> template method</a>, <a href="https://publications.waset.org/abstracts/search?q=recognition%20algorithm" title=" recognition algorithm"> recognition algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20method" title=" structured method"> structured method</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20method" title=" feature method"> feature method</a> </p> <a href="https://publications.waset.org/abstracts/138734/switching-to-the-latin-alphabet-in-kazakhstan-a-brief-overview-of-character-recognition-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3740</span> Recognizing an Individual, Their Topic of Conversation and Cultural Background from 3D Body Movement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gheida%20J.%20Shahrour">Gheida J. Shahrour</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20J.%20Russell"> Martin J. Russell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The 3D body movement signals captured during human-human conversation include clues not only to the content of people’s communication but also to their culture and personality. This paper is concerned with automatic extraction of this information from body movement signals. For the purpose of this research, we collected a novel corpus from 27 subjects, arranged them into groups according to their culture. We arranged each group into pairs and each pair communicated with each other about different topics. A state-of-art recognition system is applied to the problems of person, culture, and topic recognition. We borrowed modeling, classification, and normalization techniques from speech recognition. We used Gaussian Mixture Modeling (GMM) as the main technique for building our three systems, obtaining 77.78%, 55.47%, and 39.06% from the person, culture, and topic recognition systems respectively. In addition, we combined the above GMM systems with Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and 40.63% accuracy for person, culture, and topic recognition respectively. Although direct comparison among these three recognition systems is difficult, it seems that our person recognition system performs best for both GMM and GMM-SVM, suggesting that inter-subject differences (i.e. subject’s personality traits) are a major source of variation. When removing these traits from culture and topic recognition systems using the Nuisance Attribute Projection (NAP) and the Intersession Variability Compensation (ISVC) techniques, we obtained 73.44% and 46.09% accuracy from culture and topic recognition systems respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=person%20recognition" title="person recognition">person recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20recognition" title=" topic recognition"> topic recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=culture%20recognition" title=" culture recognition"> culture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20body%20movement%20signals" title=" 3D body movement signals"> 3D body movement signals</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20compensation" title=" variability compensation"> variability compensation</a> </p> <a href="https://publications.waset.org/abstracts/19473/recognizing-an-individual-their-topic-of-conversation-and-cultural-background-from-3d-body-movement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">541</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3739</span> Human Activities Recognition Based on Expert System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malika%20Yaici">Malika Yaici</a>, <a href="https://publications.waset.org/abstracts/search?q=Soraya%20Aloui"> Soraya Aloui</a>, <a href="https://publications.waset.org/abstracts/search?q=Sara%20Semchaoui"> Sara Semchaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recognition of human activities from sensor data is an active research area, and the main objective is to obtain a high recognition rate. In this work, we propose a recognition system based on expert systems. The proposed system makes the recognition based on the objects, object states, and gestures, taking into account the context (the location of the objects and of the person performing the activity, the duration of the elementary actions, and the activity). This work focuses on complex activities which are decomposed into simple easy to recognize activities. The proposed method can be applied to any type of activity. The simulation results show the robustness of our system and its speed of decision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20activity%20recognition" title="human activity recognition">human activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=ubiquitous%20computing" title=" ubiquitous computing"> ubiquitous computing</a>, <a href="https://publications.waset.org/abstracts/search?q=context-awareness" title=" context-awareness"> context-awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a> </p> <a href="https://publications.waset.org/abstracts/151943/human-activities-recognition-based-on-expert-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3738</span> Rational Design of Potent Compounds for Inhibiting Ca2+ -Dependent Calmodulin Kinase IIa, a Target of Alzheimer’s Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Son%20Nguyen">Son Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Thanh%20Van"> Thanh Van</a>, <a href="https://publications.waset.org/abstracts/search?q=Ly%20Le"> Ly Le</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ca2+ - dependent calmodulin kinase IIa (CaMKIIa) has recently been found to associate with protein tau missorting and polymerization in Alzheimer’s Disease (AD). However, there has yet inhibitors targeting CaMKIIa to investigate the correlation between CaMKIIa activity and protein tau polymer formation. Combining virtual screening and our statistics in binding contribution scoring function (BCSF), we rationally identified potential compounds that bind to specific CaMKIIa active site and specificity-affinity distribution of the ligand within the active site. Using molecular dynamics simulation, we identified structural stability of CaMKIIa and potent inhibitors, and site-directed bonding, separating non-specific and specific molecular interaction features. Despite of variation in confirmation of simulation time, interactions of the potent inhibitors were found to be strongly associated with the unique chemical features extracted from molecular binding poses. In addition, competitive inhibitors within CaMKIIa showed an important molecular recognition pattern toward specific ligand features. Our approach combining virtual screening with BCSF may provide an universally applicable method for precise identification in the discovery of compounds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%E2%80%99s%20disease" title="Alzheimer’s disease">Alzheimer’s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=Ca%202%2B%20-dependent%20calmodulin%20kinase%20IIa" title=" Ca 2+ -dependent calmodulin kinase IIa"> Ca 2+ -dependent calmodulin kinase IIa</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20tau" title=" protein tau"> protein tau</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20docking" title=" molecular docking"> molecular docking</a> </p> <a href="https://publications.waset.org/abstracts/84655/rational-design-of-potent-compounds-for-inhibiting-ca2-dependent-calmodulin-kinase-iia-a-target-of-alzheimers-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84655.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3737</span> Thick Disc Molecular Gas Fraction in NGC 6946</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Narendra%20Nath%20Patra">Narendra Nath Patra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Several recent studies reinforce the existence of a thick molecular disc in galaxies along with the dynamically cold thin disc. Assuming a two-component molecular disc, we model the disc of NGC 6946 as a four-component system consists of stars, HI, thin disc molecular gas, and thick disc molecular gas in vertical hydrostatic equilibrium. Following, we set up the joint Poisson-Boltzmann equation of hydrostatic equilibrium and solve it numerically to obtain a three-dimensional density distribution of different baryonic components. Using the density solutions and the observed rotation curve, we further build a three-dimensional dynamical model of the molecular disc and consecutively produce simulated CO spectral cube and spectral width profile. We find that the simulated spectral width profiles distinguishably differs for different assumed thick disc molecular gas fraction. Several CO spectral width profiles are then produced for different assumed thick disc molecular gas fractions and compared with the observed one to obtain the best fit thick disc molecular gas fraction profile. We find that the thick disc molecular gas fraction in NGC 6946 largely remains constant across its molecular disc with a mean value of 0.70 +/- 0.09. We also estimate the amount of extra-planar molecular gas in NGC 6946. We find 60% of the total molecular gas is extra-planar at the central region, whereas this fraction reduces to ~ 35% at the edge of the molecular disc. With our method, for the first time, we estimate the thick disc molecular gas fraction as a function of radius in an external galaxy with sub-kpc resolution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=galaxies%3A%20kinematics%20and%20dynamic" title="galaxies: kinematics and dynamic">galaxies: kinematics and dynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=galaxies%3A%20spiral" title=" galaxies: spiral"> galaxies: spiral</a>, <a href="https://publications.waset.org/abstracts/search?q=galaxies%3A%20structure" title=" galaxies: structure "> galaxies: structure </a>, <a href="https://publications.waset.org/abstracts/search?q=ISM%3A%20molecules" title=" ISM: molecules"> ISM: molecules</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20data" title=" molecular data"> molecular data</a> </p> <a href="https://publications.waset.org/abstracts/123278/thick-disc-molecular-gas-fraction-in-ngc-6946" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123278.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">144</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3736</span> Molecular Characterization and Identification of C-Type Lectin in Red Palm Weevil, Rhynchophorus ferrugineus Oliver</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hafiza%20Javaria%20Ashraf">Hafiza Javaria Ashraf</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinghong%20Wang"> Xinghong Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhanghong%20Shi"> Zhanghong Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Youming%20Hou"> Youming Hou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Insect’s innate immunity depends on a variety of defense responses for the recognition of invading pathogens. Pathogen recognition involves particular proteins known as pattern recognition receptors (PRRs). These PRRs interact with pathogen-associated molecular patterns (PAMPs) present on the surface of pathogens to distinguish between self and non-self. C-type lectins (CTLs) belong to a superfamily of PPRs which involved in insect immunity and defense mechanism. Rhynchophorus ferrugineus Olivier is a devastating pest of Palm cultivations in China. Although studies on R. ferrugineus immune mechanism and host defense have conducted, however, the role of CTL in immune responses of R. ferrugineus remains elusive. Here, we report RfCTL, which is a secreted protein containing a single-CRD domain. The open reading frame (ORF) of CTL is 226 bp, which encodes a putative protein of 168 amino acids. Transcript expression analysis revealed that RfCTL highly expressed in immune-related tissues, i.e., hemolymph and fat body. The abundance of RfCTL in the gut and fat body dramatically increased upon Staphylococcus aureus and Escherichia coli bacterial challenges, suggesting a role in defense against gram-positive and gram-negative bacterial infection. Taken together, we inferred that RfCTL might be involved in the immune defense of R. ferrugineus and established a solid foundation for future studies on R. ferrugineus CTL domain proteins for better understanding of insect immunity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20invasion" title="biological invasion">biological invasion</a>, <a href="https://publications.waset.org/abstracts/search?q=c-type%20lectin" title=" c-type lectin"> c-type lectin</a>, <a href="https://publications.waset.org/abstracts/search?q=insect%20immunity" title=" insect immunity"> insect immunity</a>, <a href="https://publications.waset.org/abstracts/search?q=Rhynchophorus%20ferrugineus%20Oliver" title=" Rhynchophorus ferrugineus Oliver"> Rhynchophorus ferrugineus Oliver</a> </p> <a href="https://publications.waset.org/abstracts/118350/molecular-characterization-and-identification-of-c-type-lectin-in-red-palm-weevil-rhynchophorus-ferrugineus-oliver" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118350.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">157</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3735</span> Enhanced Face Recognition with Daisy Descriptors Using 1BT Based Registration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sevil%20Igit">Sevil Igit</a>, <a href="https://publications.waset.org/abstracts/search?q=Merve%20Meric"> Merve Meric</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarp%20Erturk"> Sarp Erturk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, it is proposed to improve Daisy descriptor based face recognition using a novel One-Bit Transform (1BT) based pre-registration approach. The 1BT based pre-registration procedure is fast and has low computational complexity. It is shown that the face recognition accuracy is improved with the proposed approach. The proposed approach can facilitate highly accurate face recognition using DAISY descriptor with simple matching and thereby facilitate a low-complexity approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisy%20descriptor" title=" Daisy descriptor"> Daisy descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=One-Bit%20Transform" title=" One-Bit Transform"> One-Bit Transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20registration" title=" image registration"> image registration</a> </p> <a href="https://publications.waset.org/abstracts/12593/enhanced-face-recognition-with-daisy-descriptors-using-1bt-based-registration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12593.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3734</span> Review of Speech Recognition Research on Low-Resource Languages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=XuKe%20Cao">XuKe Cao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper reviews the current state of research on low-resource languages in the field of speech recognition, focusing on the challenges faced by low-resource language speech recognition, including the scarcity of data resources, the lack of linguistic resources, and the diversity of dialects and accents. The article reviews recent progress in low-resource language speech recognition, including techniques such as data augmentation, end to-end models, transfer learning, and multi-task learning. Based on the challenges currently faced, the paper also provides an outlook on future research directions. Through these studies, it is expected that the performance of speech recognition for low resource languages can be improved, promoting the widespread application and adoption of related technologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low-resource%20languages" title="low-resource languages">low-resource languages</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation%20techniques" title=" data augmentation techniques"> data augmentation techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a> </p> <a href="https://publications.waset.org/abstracts/193863/review-of-speech-recognition-research-on-low-resource-languages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193863.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">13</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3733</span> Modern Machine Learning Conniptions for Automatic Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jagadeesh%20Kumar">S. Jagadeesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20methods" title=" deep learning methods"> deep learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20archetypes" title=" machine learning archetypes"> machine learning archetypes</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning" title=" Bayesian learning"> Bayesian learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20and%20unsupervised%20learning" title=" supervised and unsupervised learning"> supervised and unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/71467/modern-machine-learning-conniptions-for-automatic-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3732</span> Advances in Artificial intelligence Using Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khaled%20M.%20Alhawiti">Khaled M. Alhawiti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title="speech recognition">speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=acoustic%20phonetic" title=" acoustic phonetic"> acoustic phonetic</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=hidden%20markov%20models%20%28HMM%29" title=" hidden markov models (HMM)"> hidden markov models (HMM)</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20models%20of%20speech%20recognition" title=" statistical models of speech recognition"> statistical models of speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20machine%20performance" title=" human machine performance"> human machine performance</a> </p> <a href="https://publications.waset.org/abstracts/26319/advances-in-artificial-intelligence-using-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26319.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3731</span> Biometric Recognition Techniques: A Survey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shabir%20Ahmad%20Sofi">Shabir Ahmad Sofi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shubham%20Aggarwal"> Shubham Aggarwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanyam%20Singhal"> Sanyam Singhal</a>, <a href="https://publications.waset.org/abstracts/search?q=Roohie%20Naaz"> Roohie Naaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biometric recognition refers to an automatic recognition of individuals based on a feature vector(s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. These features are used to provide an authentication for computer based security systems. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on 'who she/he is' rather than 'what she/he has' (card, token, key) or 'what she/he knows' (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal and their advantages and disadvantages, will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometric" title="biometric">biometric</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA" title=" DNA"> DNA</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title=" fingerprint"> fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=ear" title=" ear"> ear</a>, <a href="https://publications.waset.org/abstracts/search?q=face" title=" face"> face</a>, <a href="https://publications.waset.org/abstracts/search?q=retina%20scan" title=" retina scan"> retina scan</a>, <a href="https://publications.waset.org/abstracts/search?q=gait" title=" gait"> gait</a>, <a href="https://publications.waset.org/abstracts/search?q=iris" title=" iris"> iris</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20recognition" title=" voice recognition"> voice recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=unimodal%20biometric" title=" unimodal biometric"> unimodal biometric</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20biometric" title=" multimodal biometric"> multimodal biometric</a> </p> <a href="https://publications.waset.org/abstracts/15520/biometric-recognition-techniques-a-survey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">755</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3730</span> Molecular Clustering and Velocity Increase in Converging-Diverging Nozzle in Molecular Dynamics Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeoungsu%20Na">Jeoungsu Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaehawn%20Lee"> Jaehawn Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Changil%20Hong"> Changil Hong</a>, <a href="https://publications.waset.org/abstracts/search?q=Suhee%20Kim"> Suhee Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A molecular dynamics simulation in a converging-diverging nozzle was performed to study molecular collisions and their influence to average flow velocity according to a variety of vacuum levels. The static pressures and the dynamic pressure exerted by the molecule collision on the selected walls were compared to figure out the intensity variances of the directional flows. With pressure differences constant between the entrance and the exit of the nozzle, the numerical experiment was performed for molecular velocities and directional flows. The result shows that the velocities increased at the nozzle exit as the vacuum level gets higher in that area because less molecular collisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cavitation" title="cavitation">cavitation</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20collision" title=" molecular collision"> molecular collision</a>, <a href="https://publications.waset.org/abstracts/search?q=nozzle" title=" nozzle"> nozzle</a>, <a href="https://publications.waset.org/abstracts/search?q=vacuum" title=" vacuum"> vacuum</a>, <a href="https://publications.waset.org/abstracts/search?q=velocity%20increase" title=" velocity increase"> velocity increase</a> </p> <a href="https://publications.waset.org/abstracts/61069/molecular-clustering-and-velocity-increase-in-converging-diverging-nozzle-in-molecular-dynamics-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">431</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3729</span> Printed Thai Character Recognition Using Particle Swarm Optimization Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phawin%20Sangsuvan">Phawin Sangsuvan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This Paper presents the applications of Particle Swarm Optimization (PSO) Method for Thai optical character recognition (OCR). OCR consists of the pre-processing, character recognition and post-processing. Before enter into recognition process. The Character must be “Prepped” by pre-processing process. The PSO is an optimization method that belongs to the swarm intelligence family based on the imitation of social behavior patterns of animals. Route of each particle is determined by an individual data among neighborhood particles. The interaction of the particles with neighbors is the advantage of Particle Swarm to determine the best solution. So PSO is interested by a lot of researchers in many difficult problems including character recognition. As the previous this research used a Projection Histogram to extract printed digits features and defined the simple Fitness Function for PSO. The results reveal that PSO gives 67.73% for testing dataset. So in the future there can be explored enhancement the better performance of PSO with improve the Fitness Function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition%20techniques" title=" pattern recognition techniques "> pattern recognition techniques </a> </p> <a href="https://publications.waset.org/abstracts/25613/printed-thai-character-recognition-using-particle-swarm-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3728</span> Enhanced Thai Character Recognition with Histogram Projection Feature Extraction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjawan%20Rangsikamol">Benjawan Rangsikamol</a>, <a href="https://publications.waset.org/abstracts/search?q=Chutimet%20Srinilta"> Chutimet Srinilta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title="character recognition">character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20projection" title=" histogram projection"> histogram projection</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=Thai%20character%20features%20extraction" title=" Thai character features extraction "> Thai character features extraction </a> </p> <a href="https://publications.waset.org/abstracts/11674/enhanced-thai-character-recognition-with-histogram-projection-feature-extraction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11674.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">464</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3727</span> Speaker Recognition Using LIRA Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nestor%20A.%20Garcia%20Fragoso">Nestor A. Garcia Fragoso</a>, <a href="https://publications.waset.org/abstracts/search?q=Tetyana%20Baydyk"> Tetyana Baydyk</a>, <a href="https://publications.waset.org/abstracts/search?q=Ernst%20Kussul"> Ernst Kussul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article contains information from our investigation in the field of voice recognition. For this purpose, we created a voice database that contains different phrases in two languages, English and Spanish, for men and women. As a classifier, the LIRA (Limited Receptive Area) grayscale neural classifier was selected. The LIRA grayscale neural classifier was developed for image recognition tasks and demonstrated good results. Therefore, we decided to develop a recognition system using this classifier for voice recognition. From a specific set of speakers, we can recognize the speaker’s voice. For this purpose, the system uses spectrograms of the voice signals as input to the system, extracts the characteristics and identifies the speaker. The results are described and analyzed in this article. The classifier can be used for speaker identification in security system or smart buildings for different types of intelligent devices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20learning" title="extreme learning">extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=LIRA%20neural%20classifier" title=" LIRA neural classifier"> LIRA neural classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=speaker%20identification" title=" speaker identification"> speaker identification</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20recognition" title=" voice recognition"> voice recognition</a> </p> <a href="https://publications.waset.org/abstracts/112384/speaker-recognition-using-lira-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112384.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3726</span> New Approaches for the Handwritten Digit Image Features Extraction for Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20Ravi%20Babu">U. Ravi Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Mastan"> Mohd Mastan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present paper proposes a novel approach for handwritten digit recognition system. The present paper extract digit image features based on distance measure and derives an algorithm to classify the digit images. The distance measure can be performing on the thinned image. Thinning is the one of the preprocessing technique in image processing. The present paper mainly concentrated on an extraction of features from digit image for effective recognition of the numeral. To find the effectiveness of the proposed method tested on MNIST database, CENPARMI, CEDAR, and newly collected data. The proposed method is implemented on more than one lakh digit images and it gets good comparative recognition results. The percentage of the recognition is achieved about 97.32%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=handwritten%20digit%20recognition" title="handwritten digit recognition">handwritten digit recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=distance%20measure" title=" distance measure"> distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=MNIST%20database" title=" MNIST database"> MNIST database</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20features" title=" image features"> image features</a> </p> <a href="https://publications.waset.org/abstracts/40518/new-approaches-for-the-handwritten-digit-image-features-extraction-for-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40518.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">461</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3725</span> Emotion Recognition in Video and Images in the Wild</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faizan%20Tariq">Faizan Tariq</a>, <a href="https://publications.waset.org/abstracts/search?q=Moayid%20Ali%20Zaidi"> Moayid Ali Zaidi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title=" emotion recognition"> emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/152635/emotion-recognition-in-video-and-images-in-the-wild" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152635.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">187</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3724</span> An Improved Face Recognition Algorithm Using Histogram-Based Features in Spatial and Frequency Domains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiu%20Chen">Qiu Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Koji%20Kotani"> Koji Kotani</a>, <a href="https://publications.waset.org/abstracts/search?q=Feifei%20Lee"> Feifei Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Tadahiro%20Ohmi"> Tadahiro Ohmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20vector%20quantization%20%28BVQ%29" title="binary vector quantization (BVQ)">binary vector quantization (BVQ)</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT%20coefficients" title="DCT coefficients">DCT coefficients</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20patterns%20%28LBP%29" title=" local binary patterns (LBP)"> local binary patterns (LBP)</a> </p> <a href="https://publications.waset.org/abstracts/44892/an-improved-face-recognition-algorithm-using-histogram-based-features-in-spatial-and-frequency-domains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=molecular%20recognition&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=molecular%20recognition&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=molecular%20recognition&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=molecular%20recognition&page=5">5</a></li> <li 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