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Author: Jun Wan
Publisher: Springer Nature
ISBN: 3031018249
Category : Computers
Languages : en
Pages : 76
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Book Description
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.
Author: Jun Wan
Publisher: Springer Nature
ISBN: 3031018249
Category : Computers
Languages : en
Pages : 76
Get Book Here
Book Description
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.
Author: Jun Wan
Publisher: Morgan & Claypool Publishers
ISBN: 1681739232
Category : Computers
Languages : en
Pages : 90
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Book Description
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.
Author: Jun Wan
Publisher:
ISBN: 9781681739243
Category :
Languages : en
Pages : 88
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Book Description
For the last ten years, face biometric research has been intensively studied by the computer vision community. Face recognition systems have been used in mobile, banking, and surveillance systems. For face recognition systems, face spoofing attack detection is a crucial stage that could cause severe security issues in government sectors. Although effective methods for face presentation attack detection have been proposed so far, the problem is still unsolved due to the difficulty in the design of features and methods that can work for new spoofing attacks. In addition, existing datasets for studying the problem are relatively small which hinders the progress in this relevant domain. In order to attract researchers to this important field and push the boundaries of the state of the art on face anti-spoofing detection, we organized the Face Spoofing Attack Workshop and Competition at CVPR 2019, an event part of the ChaLearn Looking at People Series. As part of this event, we released the largest multi-modal face anti-spoofing dataset so far, the CASIA-SURF benchmark. The workshop reunited many researchers from around the world and the challenge attracted more than 300 teams. Some of the novel methodologies proposed in the context of the challenge achieved state-of-the-art performance. In this manuscript, we provide a comprehensive review on face anti-spoofing techniques presented in this joint event and point out directions for future research on the face anti-spoofing field.
Author: Jun Wan
Publisher: Springer Nature
ISBN: 3031329066
Category : Computers
Languages : en
Pages : 118
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Book Description
This book revises and expands upon the prior edition of Multi-Modal Face Presentation Attack Detection. The authors begin with fundamental and foundational information on face spoofing attack detection, explaining why the computer vision community has intensively studied it for the last decade. The authors also discuss the reasons that cause face anti-spoofing to be essential for preventing security breaches in face recognition systems. In addition, the book describes the factors that make it difficult to design effective methods of face presentation attack detection challenges. The book presents a thorough review and evaluation of current techniques and identifies those that have achieved the highest level of performance in a series of ChaLearn face anti-spoofing challenges at CVPR and ICCV. The authors also highlight directions for future research in face anti-spoofing that would lead to progress in the field. Additional analysis, new methodologies, and a more comprehensive survey of solutions are included in this new edition.
Author: Sébastien Marcel
Publisher: Springer
ISBN: 3319926276
Category : Computers
Languages : en
Pages : 522
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Book Description
This authoritative and comprehensive handbook is the definitive work on the current state of the art of Biometric Presentation Attack Detection (PAD) – also known as Biometric Anti-Spoofing. Building on the success of the previous, pioneering edition, this thoroughly updated second edition has been considerably expanded to provide even greater coverage of PAD methods, spanning biometrics systems based on face, fingerprint, iris, voice, vein, and signature recognition. New material is also included on major PAD competitions, important databases for research, and on the impact of recent international legislation. Valuable insights are supplied by a selection of leading experts in the field, complete with results from reproducible research, supported by source code and further information available at an associated website. Topics and features: reviews the latest developments in PAD for fingerprint biometrics, covering optical coherence tomography (OCT) technology, and issues of interoperability; examines methods for PAD in iris recognition systems, and the application of stimulated pupillary light reflex for this purpose; discusses advancements in PAD methods for face recognition-based biometrics, such as research on 3D facial masks and remote photoplethysmography (rPPG); presents a survey of PAD for automatic speaker recognition (ASV), including the use of convolutional neural networks (CNNs), and an overview of relevant databases; describes the results yielded by key competitions on fingerprint liveness detection, iris liveness detection, and software-based face anti-spoofing; provides analyses of PAD in fingervein recognition, online handwritten signature verification, and in biometric technologies on mobile devicesincludes coverage of international standards, the E.U. PSDII and GDPR directives, and on different perspectives on presentation attack evaluation. This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.
Author: Sébastien Marcel
Publisher: Springer Nature
ISBN: 9811952884
Category : Computers
Languages : en
Pages : 595
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Book Description
The third edition of this authoritative and comprehensive handbook is the definitive work on the current state of the art of Biometric Presentation Attack Detection (PAD) – also known as Biometric Anti-Spoofing. Building on the success of the previous editions, this thoroughly updated third edition has been considerably revised to provide even greater coverage of PAD methods, spanning biometrics systems based on face, fingerprint, iris, voice, vein, and signature recognition. New material is also included on major PAD competitions, important databases for research, and on the impact of recent international legislation. Valuable insights are supplied by a selection of leading experts in the field, complete with results from reproducible research, supported by source code and further information available at an associated website. Topics and features: reviews the latest developments in PAD for fingerprint biometrics, covering recent technologies like Vision Transformers, and review of competition series; examines methods for PAD in iris recognition systems, the use of pupil size measurement or multiple spectra for this purpose; discusses advancements in PAD methods for face recognition-based biometrics, such as recent progress on detection of 3D facial masks and the use of multiple spectra with Deep Neural Networks; presents an analysis of PAD for automatic speaker recognition (ASV), including a study of the generalization to unseen attacks; describes the results yielded by key competitions on fingerprint liveness detection, iris liveness detection, and face anti-spoofing; provides analyses of PAD in finger-vein recognition, in signature biometrics, and in mobile biometrics; includes coverage of international standards in PAD and legal aspects of image manipulations like morphing.This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics. This text/reference is essential reading for anyone involved in biometric identity verification, be they students, researchers, practitioners, engineers, or technology consultants. Those new to the field will also benefit from a number of introductory chapters, outlining the basics for the most important biometrics.
Author: Rui Shao
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 168
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Book Description
A significant improvement has been achieved in the visual recognition since the advent of deep convolutional neural networks (CNNs). The promising performance in visual recognition has contributed to many real-world visual applications. Face recognition, as one of the most widely used visual applications, even outperforms the human-level recognition accuracy. However, along with convenience brought by the visual applications such as face recognition, many kinds of attacks targeting at them also emerge. Specifically, face presentation attacks (i.e., print attack, video replay attack, and 3D mask attack) can easily fool many face recognition systems. More generally, adversarial attacks which add crafted imperceptible perturbations to clean images can lead general visual recognition systems into making wrong predictions. Therefore, this thesis focuses on protecting face recognition systems from the face presentation attacks and robustifying general visual recognition systems against the adversarial attacks. Various face presentation attack detection methods have been proposed for 2D attacks (i.e., print attack and video replay attack), but they cannot generalize well to unseen attacks. This thesis firstly focuses on improving the generalization ability of face presentation attack detection from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multiadversarial discriminative deep domain generalization framework. In this framework, a multi-adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Following adversarial learning based domain generalization, we also propose an adversarial learning based unsupervised domain adaptation (UDA) called Hierarchical Adversarial Deep Domain Adaptation to tackle the distribution mismatch between source and target domain. A Hierarchical Adversarial Deep Network is proposed to jointly optimize the featurelevel and pixel-level adversarial adaptation within a hierarchical network structure, which guides the knowledge from pixel-level adversarial adaptation to facilitate the feature-level adaptation and thus contributes to a better feature alignment. The above multi-adversarial deep domain generalization assumes that there exists a generalized feature space shared by multiple source domains. However, it is difficult to perfectly discover such a feature space. To circumvent this limitation, we further propose a new meta-learning framework called regularized fine-grained meta face presentation attack detection. Instead of searching a shared feature space, this framework trains our model to perform well in the simulated domain shift scenarios, which is achieved by finding generalized learning directions in the meta-learning process. Specifically, the proposed framework incorporates the domain knowledge of face presentation attack detection as the regularization so that meta-learning is conducted in the feature space regularized by the supervision of domain knowledge. Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration. Apart from defending 2D face presentation attacks, this thesis also detects 3D mask face presentation attacks. We propose a novel feature learning model to learn discriminative deep dynamic textures for 3D mask face presentation attack detection. A novel joint discriminative learning strategy is further incorporated in the learning model to jointly learn the spatial- and channel-discriminability of the deep dynamic textures. This learning strategy can be used to adaptively weight the discriminability of the learned feature from different spatial regions or channels, which i ensures that more discriminative deep dynamic textures play more important roles in face/mask classification. Besides the detection of various face presentation attacks, we have also studied the defense of adversarial attacks threatening general visual recognition systems. Specifically, we emphasize the necessity of an Open-Set Adversarial Defense (OSAD) mechanism, which defends adversarial attacks under an open-set setting. We propose an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem. The proposed network uses an encoder with feature-denoising layers coupled with a classifier to learn a noise-free latent feature representation. Several techniques are further employed for the solution. First, a decoder is utilized to ensure that clean images can be reconstructed from the obtained latent features. Then, self-supervision is used to ensure that the latent features are informative enough to carry out an auxiliary task. Finally, to exploit more complementary knowledge from clean image classification to facilitate feature denoising and search a more generalized local minimum for open-set recognition, we further propose clean-adversarial mutual learning, in which a peer network (classifying clean images) is further introduced to mutually learn with the classifier (classifying adversarial images). In short, the major contributions of this thesis are summarized as follows. A multi-adversarial discriminative deep domain generalization framework is proposed to improve the generalization ability of face presentation attack detection method to unseen attacks, which learns a discriminative and shared feature space among multiple source domains via adversarial learning. An adversarial learning based UDA method named as Hierarchical Adversarial Deep Domain Adaptation is also proposed to adapt the model trained with source data to perform well on target data with different distributions. A regularized fine-grained meta face presentation attack detection method is proposed to train the face presentation attack detection model to learn to generalize well to unseen attacks, which simultaneously conducts metaiv learning in a variety of domain shift scenarios under face presentation attacks. A joint discriminative learning of deep dynamic textures is proposed to capture subtle facial motion differences with spatial- and channel- discriminability for 3D mask presentation attack detection. A new research problem called Open-Set Adversarial Defense (OSAD) is introduced to study the adversarial defense under the open-set setting. An Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDNCAML) method is proposed as a solution to the OSAD problem, which simultaneously detects open-set samples and classifies known classes in the presence of adversarial noise.
Author: Jennifer Hamblin
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multi-spectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack detection systems may be discriminative, the need for additional sensors and computational resources substantially increases complexity and costs. Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems. We introduce (1) a new cross-domain presentation attack detection framework that increases the separability of bonafide and presentation attacks using only visible spectrum imagery, (2) an inverse domain regularization technique for added training stability when optimizing our cross-domain presentation attack detection framework, and (3) a dense domain adaptation subnetwork to transform representations between visible and non-visible domains.
Author: Christian Rathgeb
Publisher: Springer Nature
ISBN: 3030876640
Category : Computers
Languages : en
Pages : 487
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Book Description
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area.
Author: Sébastien Marcel
Publisher: Springer
ISBN: 1447165241
Category : Computers
Languages : en
Pages : 290
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Book Description
Presenting the first definitive study of the subject, this Handbook of Biometric Anti-Spoofing reviews the state of the art in covert attacks against biometric systems and in deriving countermeasures to these attacks. Topics and features: provides a detailed introduction to the field of biometric anti-spoofing and a thorough review of the associated literature; examines spoofing attacks against five biometric modalities, namely, fingerprints, face, iris, speaker and gait; discusses anti-spoofing measures for multi-model biometric systems; reviews evaluation methodologies, international standards and legal and ethical issues; describes current challenges and suggests directions for future research; presents the latest work from a global selection of experts in the field, including members of the TABULA RASA project.
