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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

Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to wearing a mask presents an emerging challenge for face recognition systems. In this paper, we present a solution to improve masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities. The achieved evaluation results on three face recognition models, two real masked datasets, and two synthetically generated masked face datasets proved that our proposed approach significantly improves the performance in most experimental settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Iris presentation attack detection by attention-based and deep pixel-wise binary supervision network

arXiv

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arXiv 2021年

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

Iris presentation attack detection (PAD) plays a vital role in iris recognition systems. Most existing CNN-based iris PAD solutions 1) perform only binary label supervision during the training of CNNs, serving global information learning but weakening the capture of local discriminative features, 2) prefer the stacked deeper convolutions or expert-designed networks, raising the risk of overfitting, 3) fuse multiple PAD systems or various types of features, increasing difficulty for deployment on mobile devices. Hence, we propose a novel attention-based deep pixel-wise binary supervision (A-PBS) method. Pixel-wise supervision is first able to capture the fine-grained pixel/patch-level cues. Then, the attention mechanism guides the network to automatically find regions that most contribute to an accurate PAD decision. Extensive experiments are performed on LivDet-Iris 2017 and three other publicly available databases to show the effectiveness and robustness of proposed A-PBS methods. For instance, the A-PBS model achieves an HTER of 6.50% on the IIITD-WVU database outperforming state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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ElasticFace: Elastic Margin Loss for Deep Face Recognition

arXiv

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arXiv 2021年

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

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Masked Face Recognition: Human vs. Machine

arXiv

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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 Presentation Attack Detection of Iris Recognition Systems

Demographic Bias in Presentation Attack Detection of Iris Re...

引用

European Signal Processing Conference (EUSIPCO)

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

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

With the widespread use of biometric systems, the demographic bias problem raises more attention. Although many studies addressed bias issues in biometric verification, there are no works that analyze 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 problem. We study the bias in iris PAD using three baselines (hand-crafted, transfer-learning, and training from scratch) using the NDCLD-2013 [18] database. The experimental results point out that female users will be significantly less protected by the PAD, in comparison to males.

关键词： Training Databases Training data Signal processing algorithms Europe Signal processing Iris recognition

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Micro Stripes Analyses for Iris Presentation Attack Detection

Micro Stripes Analyses for Iris Presentation Attack Detectio...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Naser Damer Florian Kirchbuchner Arjan Kuijper Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

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.

关键词： Iris Lenses Iris recognition Databases Feature extraction Image segmentation Neural networks

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Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

Face Quality Estimation and Its Correlation to Demographic a...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Philipp Terhörst Jan Niklas Kolf Naser Damer Florian Kirchbuchner Arjan Kuijper Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embeddings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.

关键词： Face recognition Quality assessment Robustness Estimation Adaptation models Image quality Correlation

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Beyond Identity: What Information Is Stored in Biometric Face Templates?

Beyond Identity: What Information Is Stored in Biometric Fac...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Philipp Terhörst Daniel Fährmann Naser Damer Florian Kirchbuchner Arjan Kuijper Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates.

关键词： Face recognition Reliability Faces Predictive models Lighting Training Stochastic processes

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Demographic bias in biometrics: A survey on an emerging challenge

IEEE Transactions on Technology and Society

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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

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On Benchmarking Iris Recognition within a Head-mounted Display for AR/VR Applications

On Benchmarking Iris Recognition within a Head-mounted Displ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Fadi Boutros Naser Damer Kiran Raja Raghavendra Ramachandra Florian Kirchbuchner Arjan Kuijper Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Norwegian Biometrics Laboratory NTNU Gjovik Norway

ISBN: (数字)9781728191867

ISBN: (纸本)9781728191874

Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafted feature extraction approaches, and to complement it, we also present the analysis using four deep learning models. While taking into consideration the minimalistic hardware requirements of stand-alone HMD, we employ and adapt a recently developed miniature segmentation model (EyeMMS) for segmenting the iris. Further, to account for non-ideal and non-collaborative capture of iris, we define a new iris quality metric that we termed as Iris Mask Ratio (IMR) to quantify the iris recognition performance. Motivated by the performance of iris recognition, we also propose the continuous authentication of users in a non-collaborative capture setting in HMD. Through the experiments on a publicly available OpenEDS dataset, we show that performance with EER = 5% can be achieved using deep learning methods in a general setting, along with high accuracy for continuous user authentication.

关键词： Iris recognition Resists Feature extraction Image segmentation Cameras Pupils Authentication

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