Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis

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Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis</title> <meta name="description" content="Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis"> <meta name="keywords" content="biometrics, finger vein recognition, principal component analysis (PCA), kernel principal component analysis (KPCA)"> <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"> 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87362</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Hajian">Amir Hajian</a>, <a href="https://publications.waset.org/abstracts/search?q=Sepehr%20Damavandinejadmonfared"> Sepehr Damavandinejadmonfared</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA. <iframe src="https://publications.waset.org/abstracts/14476.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=finger%20vein%20recognition" title=" finger vein recognition"> finger vein recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20component%20analysis%20%28PCA%29" title=" principal component analysis (PCA)"> principal component analysis (PCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20principal%20component%20analysis%20%28KPCA%29" title=" kernel principal component analysis (KPCA)"> kernel principal component analysis (KPCA)</a> </p> <a href="https://publications.waset.org/abstracts/14476/optimal-feature-extraction-dimension-in-finger-vein-recognition-using-kernel-principal-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14476.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">365</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis