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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10481">arXiv:2409.10481</a> <span> [<a href="https://arxiv.org/pdf/2409.10481">pdf</a>, <a href="https://arxiv.org/format/2409.10481">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=La+Cava%2C+S+M">Simone Maurizio La Cava</a>, <a href="/search/cs?searchtype=author&query=Concas%2C+S">Sara Concas</a>, <a href="/search/cs?searchtype=author&query=Tolosana%2C+R">Ruben Tolosana</a>, <a href="/search/cs?searchtype=author&query=Casula%2C+R">Roberto Casula</a>, <a href="/search/cs?searchtype=author&query=Orr%C3%B9%2C+G">Giulia Orr霉</a>, <a href="/search/cs?searchtype=author&query=Drahansky%2C+M">Martin Drahansky</a>, <a href="/search/cs?searchtype=author&query=Fierrez%2C+J">Julian Fierrez</a>, <a href="/search/cs?searchtype=author&query=Marcialis%2C+G+L">Gian Luca Marcialis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.10481v1-abstract-short" style="display: inline;"> 3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's characteristics, are different than expected, as typically happens in video surveillance. Additionally, 3DFR algorithms follow various strategies to addres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10481v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10481v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10481v1-abstract-full" style="display: none;"> 3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. These assumptions limit their use when acquisition conditions, such as the subject's distance from the camera or the camera's characteristics, are different than expected, as typically happens in video surveillance. Additionally, 3DFR algorithms follow various strategies to address the reconstruction of a 3D shape from 2D data, such as statistical model fitting, photometric stereo, or deep learning. In the present study, we explore the application of three 3DFR algorithms representative of the SOTA, employing each one as the template set generator for a face verification system. The scores provided by each system are combined by score-level fusion. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera and camera characteristics (cross-distance and cross-camera settings), thus encouraging further investigations on multiple 3DFR-based approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10481v1-abstract-full').style.display = 'none'; document.getElementById('2409.10481v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted at T-CAP - Towards a Complete Analysis of People: Fine-grained Understanding for Real-World Applications, workshop in conjunction with the 18th European Conference on Computer Vision ECCV 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.04580">arXiv:2404.04580</a> <span> [<a href="https://arxiv.org/pdf/2404.04580">pdf</a>, <a href="https://arxiv.org/format/2404.04580">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> SDFR: Synthetic Data for Face Recognition Competition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shahreza%2C+H+O">Hatef Otroshi Shahreza</a>, <a href="/search/cs?searchtype=author&query=Ecabert%2C+C">Christophe Ecabert</a>, <a href="/search/cs?searchtype=author&query=George%2C+A">Anjith George</a>, <a href="/search/cs?searchtype=author&query=Unnervik%2C+A">Alexander Unnervik</a>, <a href="/search/cs?searchtype=author&query=Marcel%2C+S">S茅bastien Marcel</a>, <a href="/search/cs?searchtype=author&query=Di+Domenico%2C+N">Nicol貌 Di Domenico</a>, <a href="/search/cs?searchtype=author&query=Borghi%2C+G">Guido Borghi</a>, <a href="/search/cs?searchtype=author&query=Maltoni%2C+D">Davide Maltoni</a>, <a href="/search/cs?searchtype=author&query=Boutros%2C+F">Fadi Boutros</a>, <a href="/search/cs?searchtype=author&query=Vogel%2C+J">Julia Vogel</a>, <a href="/search/cs?searchtype=author&query=Damer%2C+N">Naser Damer</a>, <a href="/search/cs?searchtype=author&query=S%C3%A1nchez-P%C3%A9rez%2C+%C3%81">脕ngela S谩nchez-P茅rez</a>, <a href="/search/cs?searchtype=author&query=EnriqueMas-Candela"> EnriqueMas-Candela</a>, <a href="/search/cs?searchtype=author&query=Calvo-Zaragoza%2C+J">Jorge Calvo-Zaragoza</a>, <a href="/search/cs?searchtype=author&query=Biesseck%2C+B">Bernardo Biesseck</a>, <a href="/search/cs?searchtype=author&query=Vidal%2C+P">Pedro Vidal</a>, <a href="/search/cs?searchtype=author&query=Granada%2C+R">Roger Granada</a>, <a href="/search/cs?searchtype=author&query=Menotti%2C+D">David Menotti</a>, <a href="/search/cs?searchtype=author&query=DeAndres-Tame%2C+I">Ivan DeAndres-Tame</a>, <a href="/search/cs?searchtype=author&query=La+Cava%2C+S+M">Simone Maurizio La Cava</a>, <a href="/search/cs?searchtype=author&query=Concas%2C+S">Sara Concas</a>, <a href="/search/cs?searchtype=author&query=Melzi%2C+P">Pietro Melzi</a>, <a href="/search/cs?searchtype=author&query=Tolosana%2C+R">Ruben Tolosana</a>, <a href="/search/cs?searchtype=author&query=Vera-Rodriguez%2C+R">Ruben Vera-Rodriguez</a>, <a href="/search/cs?searchtype=author&query=Perelli%2C+G">Gianpaolo Perelli</a> , et al. (3 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.04580v2-abstract-short" style="display: inline;"> Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04580v2-abstract-full').style.display = 'inline'; document.getElementById('2404.04580v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.04580v2-abstract-full" style="display: none;"> Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.04580v2-abstract-full').style.display = 'none'; document.getElementById('2404.04580v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15578">arXiv:2309.15578</a> <span> [<a href="https://arxiv.org/pdf/2309.15578">pdf</a>, <a href="https://arxiv.org/ps/2309.15578">ps</a>, <a href="https://arxiv.org/format/2309.15578">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Micheletto%2C+M">Marco Micheletto</a>, <a href="/search/cs?searchtype=author&query=Casula%2C+R">Roberto Casula</a>, <a href="/search/cs?searchtype=author&query=Orr%C3%B9%2C+G">Giulia Orr霉</a>, <a href="/search/cs?searchtype=author&query=Carta%2C+S">Simone Carta</a>, <a href="/search/cs?searchtype=author&query=Concas%2C+S">Sara Concas</a>, <a href="/search/cs?searchtype=author&query=La+Cava%2C+S+M">Simone Maurizio La Cava</a>, <a href="/search/cs?searchtype=author&query=Fierrez%2C+J">Julian Fierrez</a>, <a href="/search/cs?searchtype=author&query=Marcialis%2C+G+L">Gian Luca Marcialis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15578v1-abstract-short" style="display: inline;"> The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, Liveness Detection in Action and Fingerprint Representation, to evaluate the efficacy of PAD embedded in verification systems and t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15578v1-abstract-full').style.display = 'inline'; document.getElementById('2309.15578v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15578v1-abstract-full" style="display: none;"> The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, Liveness Detection in Action and Fingerprint Representation, to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, hidden challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15578v1-abstract-full').style.display = 'none'; document.getElementById('2309.15578v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 10 tables, IEEE International Joint Conference on Biometrics (IJCB 2023)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.11357">arXiv:2309.11357</a> <span> [<a href="https://arxiv.org/pdf/2309.11357">pdf</a>, <a href="https://arxiv.org/format/2309.11357">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> 3D Face Reconstruction: the Road to Forensics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=La+Cava%2C+S+M">Simone Maurizio La Cava</a>, <a href="/search/cs?searchtype=author&query=Orr%C3%B9%2C+G">Giulia Orr霉</a>, <a href="/search/cs?searchtype=author&query=Drahansky%2C+M">Martin Drahansky</a>, <a href="/search/cs?searchtype=author&query=Marcialis%2C+G+L">Gian Luca Marcialis</a>, <a href="/search/cs?searchtype=author&query=Roli%2C+F">Fabio Roli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.11357v1-abstract-short" style="display: inline;"> 3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11357v1-abstract-full').style.display = 'inline'; document.getElementById('2309.11357v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.11357v1-abstract-full" style="display: none;"> 3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.11357v1-abstract-full').style.display = 'none'; document.getElementById('2309.11357v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The manuscript has been accepted for publication in ACM Computing Surveys. arXiv admin note: text overlap with arXiv:2303.11164</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.11164">arXiv:2303.11164</a> <span> [<a href="https://arxiv.org/pdf/2303.11164">pdf</a>, <a href="https://arxiv.org/ps/2303.11164">ps</a>, <a href="https://arxiv.org/format/2303.11164">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICPR56361.2022.9956031">10.1109/ICPR56361.2022.9956031 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> 3D Face Reconstruction for Forensic Recognition -- A Survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=La+Cava%2C+S+M">Simone Maurizio La Cava</a>, <a href="/search/cs?searchtype=author&query=Orr%C3%B9%2C+G">Giulia Orr霉</a>, <a href="/search/cs?searchtype=author&query=Goldmann%2C+T">Tom谩拧 Goldmann</a>, <a href="/search/cs?searchtype=author&query=Drahansky%2C+M">Martin Drahansky</a>, <a href="/search/cs?searchtype=author&query=Marcialis%2C+G+L">Gian Luca Marcialis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.11164v1-abstract-short" style="display: inline;"> 3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11164v1-abstract-full').style.display = 'inline'; document.getElementById('2303.11164v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.11164v1-abstract-full" style="display: none;"> 3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11164v1-abstract-full').style.display = 'none'; document.getElementById('2303.11164v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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