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Learnable multi-level frequency decomposition and hierarchical attention mechanism for generalized face presentation attack detection

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arXiv 2021年

作者： Fang, Meiling Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Mathematical and Applied Visual Computing Tu Darmstadt Darmstadt Germany

With the increased deployment of face recognition systems in our daily lives, face presentation attack detection (PAD) is attracting a lot of attention and playing a key role in securing face recognition systems. Despite the great performance achieved by the hand-crafted and deep learning based methods in intra-dataset evaluations, the performance drops when dealing with unseen scenarios. In this work, we propose a dual-stream convolution neural networks (CNNs) framework. One stream adapts four learnable frequency filters to learn features in the frequency domain, which are less influenced variations in sensors/ illuminations. The other stream leverage the RGB images to complement the features of the frequency domain. Moreover, we propose a hierarchical attention module integration to join the information from the two streams at different stages by considering the nature of deep features in different layers of the CNN. The proposed method is evaluated in the intra-dataset and cross-dataset setups and the results demonstrates that our proposed approach enhances the generalizability in most experimental setups in comparison to state-of-the-art, including the methods designed explicitly for domain adaption/shift problem. We successfully prove the design of our proposed PAD solution in a stepwise ablation study that involves our proposed learnable frequency decomposition, our hierarchical attention module design, and the used loss function. Training codes and pre-trained models are publicly released. Copyright © 2021, The Authors. All rights reserved.

关键词： Frequency domain analysis

Real masks and spoof faces: On the masked face presentation attack detection

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arXiv 2021年

作者： Fang, Meiling Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

Face masks have become one of the main methods for reducing the transmission of COVID-19. This makes face recognition (FR) a challenging task because masks hide several discriminative features of faces. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to the growing number of masked FR studies, the impact of face masked attacks on PAD has not been explored. Therefore, we present novel attacks with real face masks placed on presentations and attacks with subjects wearing masks to reflect the current real-world situation. Furthermore, this study investigates the effect of masked attacks on PAD performance by using seven state-of-the-art PAD algorithms under different experimental settings. We also evaluate the vulnerability of FR systems to masked attacks. The experiments show that real masked attacks pose a serious threat to the operation and security of FR systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

ElasticFace: Elastic Margin Loss for Deep Face Recognition

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arXiv 2021年

作者： Boutros, Fadi Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs are publicly released https://***/fdbtrs/ElasticFace. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

Masked Face Recognition: Human vs. Machine

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arXiv 2021年

作者： Damer, Naser Boutros, Fadi Süßmilch, Marius Fang, Meiling Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR solutions. However, such solutions can fail in certain processes, leading to performing the verification task by a human expert. This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic FR solutions. This involves an extensive evaluation by human experts and 4 automatic recognition solutions. The study concludes with a set of take-home messages on different aspects of the correlation between the verification behavior of humans and machines. Copyright © 2021, The Authors. All rights reserved.

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Demographic bias in biometrics: A survey on an emerging challenge

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IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2020年第2期1卷 89-103页

作者： Drozdowski, Pawel Rathgeb, Christian Dantcheva, Antitza Damer, Naser Busch, Christoph da/sec—Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt64295 Germany Inria Sophia Antipolis06904 France Fraunhofer Institute for Computer Graphics Research IGD Darmstadt64283 Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt64289 Germany

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as "racist" or "biased" by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics;2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation;3) a discussion of the pertinent technical and social matters;and 4) an outline of the remaining challenges and future work items, both from technological and social points of view. © 2020 IEEE. All right reserved.

关键词： Biometrics

Demographic bias in presentation attack detection of iris recognition systems

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arXiv 2020年

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there is no works that analyse the bias in presentation attack detection (PAD) decisions. Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper. To enable a clear discussion, we adapt the notions of differential performance and differential outcome to the PAD *** study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the the NDCLD-2013 [16] database. The experimental results points out that female users will be significantly less protected by the PAD, in comparison to males. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study

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The Effect of Wearing a Mask on Face Recognition Performance...

International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Naser Damer Jonas Henry Grebe Cong Chen Fadi Boutros Florian Kirchbuchner Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

Face recognition has become essential in our daily lives as a convenient and contactless method of accurate identity verification. Process such as identity verification at automatic border control gates or the secure login to electronic devices are increasingly dependant on such technologies. The recent COVID-19 pandemic have increased the value of hygienic and contactless identity verification. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition in a collaborative environment is currently sensitive yet understudied issue. We address that by presenting a specifically collected database containing three session, each with three different capture instructions, to simulate realistic use cases. We further study the effect of masked face probes on the behaviour of three top-performing face recognition systems, two academic solutions and one commercial off-the-shelf (COTS) system.

关键词： Faces Face recognition Databases Lighting Probes Videos Collaboration

Micro Stripes Analyses for Iris Presentation Attack Detection

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arXiv 2020年

Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our Presentation Attack Detection (PAD) network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art (SoTA) algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations. Copyright © 2020, The Authors. All rights reserved.

关键词： Contact lenses

The effect of wearing a mask on face recognition performance: an exploratory study

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arXiv 2020年

作者： Damer, Naser Grebe, Jonas Henry Chen, Cong Boutros, Fadi Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

关键词： Face recognition