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Facial expressions as a vulnerability in face recognition

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arXiv 2020年

作者： Peña, Alejandro Morales, Aythami Serna, Ignacio Fierrez, Julian Lapedriza, Agata Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Computer Science Dpt. eHealth Center Universitat Oberta de Catalunya Spain

This work explores facial expression bias as a security vulnerability of face recognition systems. Despite the great performance achieved by state-of-the-art face recognition systems, the algorithms are still sensitive to a large range of covariates. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies. Our study analyzes: i) facial expression biases in the most popular face recognition databases;and ii) the impact of facial expression in face recognition performances. Our experimental framework includes two face detectors, three face recognition models, and three different databases. Our results demonstrate a huge facial expression bias in the most widely used databases, as well as a related impact of face expression in the performance of state-of-the-art algorithms. This work opens the door to new research lines focused on mitigating the observed vulnerability. © 2020, CC BY-NC-ND.

关键词： Face recognition

Exploring Body Texture From mmW Images for Person Recognition

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IEEE Transactions on Biometrics, Behavior, and Identity Scie...

IEEE Transactions on biometrics, Behavior, and Identity Science 2019年第2期1卷 139-151页

作者： Ester Gonzalez-Sosa Ruben Vera-Rodriguez Julian Fierrez Fernando Alonso-Fernandez Vishal M. Patel Biometrics and Data Pattern Analytics Lab-ATVS Escuela Politecnica Superior Universidad Autonoma de Madrid Madrid Spain School of Information Technology Halmstad University Halmstad Sweden Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

Imaging using millimeter waves (mmWs) has many advantages including the ability to penetrate obscurants, such as clothes and polymers. After having explored shape information retrieved from mmW images for person recognition, in this paper we aim to gain some insight about the potential of using mmW texture information for the same task, considering not only the mmW face, but also mmW torso and mmW wholebody. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained convolutional neural networks (CNNs) models. First, we analyze the individual performance of three mmW body parts, concluding that: 1) mmW torso region is more discriminative than mmW face and the whole body; 2) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body; and 3) hand-crafted features slightly outperform CNN features on mmW torso. In the second part of this paper, we analyze different multi-algorithmic and multi-modal techniques, including a novel CNN-based fusion technique, improving verification results to 2% EER and identification rank-1 results up to 99%. Comparative analyses with mmW body shape information and face recognition in the visible and NIR spectral bands are also reported.

关键词： Feature extraction Torso Face Histograms biometrics (access control) Shape

SensitiveNets: Learning agnostic representations with application to face recognition

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arXiv 2019年

作者： Morales, Aythami Fierrez, Julian Vera-Rodriguez, Ruben Biometric and Data Pattern Analytics Lab-BiDA Escuela Politecnica Superior Universi-dad Autónoma de Madrid Campus de Cantoblanco C/ Francisco Tomás y Valiente 11 Madrid28049 Spain

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

Lecture Notes in Computer Science (including subseries Lectu...

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Preface

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2019年 11401 LNCS卷 vi页

作者： Fierrez, Julian Vera-Rodriguez, Ruben Morales, Aythami Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Madrid Spain

SVC-onGoing: Signature Verification Competition

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arXiv 2021年

作者： Tolosana, Ruben Vera-Rodriguez, Ruben Gonzalez-Garcia, Carlos Fierrez, Julian Morales, Aythami Ortega-Garcia, Javier Ruiz-Garcia, Juan Carlos Romero-Tapiador, Sergio Rengifo, Santiago Caruana, Miguel Jiang, Jiajia Lai, Songxuan Jin, Lianwen Zhu, Yecheng Galbally, Javier Diaz, Moises Ferrer, Miguel Angel Gomez-Barrero, Marta Hodashinsky, Ilya Sarin, Konstantin Slezkin, Artem Bardamova, Marina Svetlakov, Mikhail Saleem, Mohammad Szücs, Cintia Lia Kovari, Bence Pulsmeyer, Falk Wehbi, Mohamad Zanca, Dario Ahmad, Sumaiya Mishra, Sarthak Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Spain South China University of Technology China European Commission - Joint Research Centre Italy Universidad de las Palmas de Gran Canaria Spain Hochschule Ansbach Germany Tomsk State University of Control Systems and Radioelectronics Russia Budapest University of Technology and Economics Hungary Machine Learning and Data Analytics Lab FAU Germany Jamia Millia Islamia India

This article presents SVC-onGoing, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB and SVC2021_EvalDB, and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition. © 2021, CC BY-NC-ND.

关键词： Deep learning

IJCB 2022 Mobile Behavioral biometrics Competition (MobileB2C)

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IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2...

IEEE International Joint Conference on biometrics (IJCB)

作者： Giuseppe Stragapede Ruben Vera-Rodriguez Ruben Tolosana Aythami Morales Julian Fierrez Javier Ortega-Garcia Sanka Rasnayaka Sachith Seneviratne Vipula Dissanayake Jonathan Liebers Ashhadul Islam Samir Brahim Belhaouari Sumaiya Ahmad Suraiya Jabin Biometrics and Data Pattern Analytics Lab UAM Madrid Spain National University of Singapore Singapore University of Melbourne Melbourne Australia University of Auckland Auckland New Zealand University of Duisburg-Essen Duisburg Germany Hamad Bin Khalifa University ar-Rayyan Qatar Jamia Millia Islamia New Delhi India

ISBN: (纸本)9781665463959

This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral biometrics Competition (MobileB2C). The aim of MobileB2C is bench-marking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB 1 1 https://***/bidalab/MobileB2C_BehavePassDE, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. “Random” (different users with different devices) and “skilled” (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a non-trivial challenge. MobileB2C will be established as an on-going competition 2 2 https://***/view/mobileb2c/.

关键词： Human computer interaction Protocols biometrics (access control) databases Authentication Touch sensitive screens Mobile handsets

Kinematic Synthesis for 3D Signatures

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Kinematic Synthesis for 3D Signatures

IEEE International Joint Conference on biometrics (IJCB)

作者： Moises Diaz Miguel A. Ferrer Cristina Carmona-Duarte Jose Juan Quintana Aythami Morales Julian Fierrez Réjean Plamondon Instituto Universitario para el Desarrollo Tecnológico y la Innovación en Comunicaciones (iDeTIC) Universidad de Las Palmas de Gran Canaria Spain Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain École Polytechnique University of Montreal Montreal QC Canada

ISBN: (纸本)9781665463959

This paper proposes a method to generate the synthetic kinematic of signatures in 3D. The analysis of 3D signatures is becoming a hot topic due to the irruption of commercial off-the-shelf devices for easy acquisition of 3D movements. However, the novelty of this technology reveals the scarce publicly available signatures in 3D, which hinder their de-velopment. A solution is the synthesis of Signatures in 3D. As a first step, this paper synthesizes the kinematics of 3D signatures based on the Kinematic Theory of Rapid Movements and its associated Sigma-Lognormal model in 3D. To evaluate the method, we regenerate signature databases with synthetic speed profiles in all genuine and forgeries found in two 3D signature databases. Then, we analyze the similarities in the performance of a signature verifier when real and synthetic signatures are used in random and skilled forgeries experiments.

关键词： Solid modeling Three-dimensional displays databases biometrics (access control) Estimation Kinematics Forgery

IJCB 2022 Mobile Behavioral biometrics Competition (MobileB2C)

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arXiv 2022年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Fierrez, Julian Ortega-Garcia, Javier Rasnayaka, Sanka Seneviratne, Sachith Dissanayake, Vipula Liebers, Jonathan Islam, Ashhadul Belhaouari, Samir Brahim Ahmad, Sumaiya Jabin, Suraiya Biometrics and Data Pattern Analytics Lab UAM Madrid Spain National University of Singapore Singapore University of Melbourne Melbourne Australia University of Auckland Auckland New Zealand University of Duisburg-Essen Duisburg Germany Hamad Bin Khalifa University ar-Rayyan Qatar Jamia Millia Islamia New Delhi India

This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral biometrics Competition (MobileB2C). The aim of MobileB2C is benchmarking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB1, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. "Random" (different users with different devices) and "skilled" (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a nontrivial challenge. MobileB2C will be established as an on-going competition 2 © 2022, CC BY-NC-ND.

关键词： Authentication

IEEE Bigdata 2023 Keystroke Verification Challenge (KVC)

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IEEE BigData 2023 Keystroke Verification Challenge (KVC)

IEEE International Conference on Big data

作者： Giuseppe Stragapede Ruben Vera-Rodriguez Ruben Tolosana Aythami Morales Ivan DeAndres-Tame Naser Damer Julian Fierrez Javier-Ortega Garcia Nahuel Gonzalez Andrei Shadrikov Dmitrii Gordin Leon Schmitt Daniel Wimmer Christoph Großmann Joerdis Krieger Florian Heinz Ron Krestel Christoffer Mayer Simon Haberl Helena Gschrey Yosuke Yamagishi Sanjay Saha Sanka Rasnayaka Sandareka Wickramanayake Terence Sim Weronika Gutfeter Adam Baran Mateusz Krzysztón Przemysław Jaskóła Biometrics and Data Pattern Analytics (BiDA) Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Laboratorio de Sistemas de Información Avanzados (LSIA) Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Almaty Kazakhstan Regensburg University Of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Warsaw Poland

Institute, Warsaw, Poland This paper describes the results of the IEEE Bigdata 2023 Keystroke Verification Challenge 1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on Codalab 2 , the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.

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