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40th German Conference on Pattern Recognition, GCPR 2018

作者： Damer, Naser Boller, Viola Wainakh, Yaza Boutros, Fadi Terhörst, Philipp Braun, Andreas Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783030129385

Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features. © 2019, Springer Nature Switzerland AG.

关键词： Probes

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Compact Models for Periocular Verification Through Knowledge Distillation

Compact Models for Periocular Verification Through Knowledge...

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International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Fadi Boutros Naser Damer Meiling Fang Kiran Raja Florian Kirchbuchner Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany The Norwegian Colour and Visual Computing Laboratory NTNU Gjovik Norway

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

Despite the wide use of deep neural network for periocular verification, achieving smaller deep learning models with high performance that can be deployed on low computational powered devices remains a challenge. In term of computation cost, we present in this paper a lightweight deep learning model with only 1.1m of trainable parameters, DenseNet-20, based on DenseNet architecture. Further, we present an approach to enhance the verification performance of DenseNet-20 via knowledge distillation. With the experiments on VISPI dataset captured with two different smartphones, iPhone and Nokia, we show that introducing knowledge distillation to DenseNet-20 training phase outperforms the same model trained without knowledge distillation where the Equal Error Rate (EER) reduces from 8.36% to 4.56% EER on iPhone data, from 5.33% to 4.64% EER on Nokia data, and from 20.98% to 15.54% EER on cross-smartphone data.

关键词： Smart phones Computational modeling Training Performance evaluation Databases Feature extraction Lighting

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Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies

Privacy Evaluation Protocols for the Evaluation of Soft-Biom...

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International Conference of the Biometrics Special Interest Group (BIOSIG)

作者： Philipp Terhörst Marco Huber Naser Damer Peter Rot Florian Kirchbuchner Vitomir Struc Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

ISBN: (数字)9783885797005

ISBN: (纸本)9781728189277

Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs`s principle of cryptography.

关键词： Protocols Faces Privacy Training Creep Data privacy Face recognition

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On benchmarking iris recognition within a Head-Mounted Display for AR/VR applications

arXiv

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arXiv 2020年

作者： Boutros, Fadi Damer, Naser Raja, Kiran Ramachandra, Raghavendra Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Norwegian Colour and Visual Computing Laboratory NTNU Gjovik Norway Norwegian Biometrics Laboratory NTNU Gjovik Norway

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. Copyright © 2020, The Authors. All rights reserved.

关键词： Helmet mounted displays

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A template consensus method for visual tracking

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Optoelectronics Letters 2019年第1期15卷 70-74页

作者： ZHOU Tong-xue ZENG Dong-dong ZHU Ming Arjan Kuijper hangchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033China The University of the Chinese Academy of Sciences Beijing 100049China Department of Graphic Interactive System and Mathematical and Applied Visual Computing Fraunhofer Institute for Computer Graphics Research IGDDarmstadt 64283Germany

visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot.

关键词： method tracking challenging

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Periocular Biometrics in Head-Mounted Displays: A Sample Selection Approach for Better Recognition

Periocular Biometrics in Head-Mounted Displays: A Sample Sel...

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International Workshop on Biometrics and Forensics (IWBF)

作者： Fadi Boutros Naser Damer Kiran Raja Raghavendra Ramachandra Florian Kirchbuchner Arjan Kuijper Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany The Norwegian Colour and Visual Computing Laboratory NTNU Gjovik Norway Norwegian Biometrics Laboratory NTNU Gjovik Norway

ISBN: (数字)9781728162324

ISBN: (纸本)9781728162331

Virtual and augmented reality technologies are increasingly used in a wide range of applications. Such technologies employ a Head Mounted Display (HMD) that typically includes an eye-facing camera and is used for eye tracking. As some of these applications require accessing or transmitting highly sensitive private information, a trusted verification of the operator's identity is needed. We investigate the use of HMD-setup to perform verification of operator using periocular region captured from inbuilt camera. However, the uncontrolled nature of the periocular capture within the HMD results in images with a high variation in relative eye location and eye-opening due to varied interactions. Therefore, we propose a new normalization scheme to align the ocular images and then, a new reference sample selection protocol to achieve higher verification accuracy. The applicability of our proposed scheme is exemplified using two handcrafted feature extraction methods and two deep-learning strategies. We conclude by stating the feasibility of such a verification approach despite the uncontrolled nature of the captured ocular images, especially when proper alignment and sample selection strategy is employed.

关键词： Biometrics periocular head-mounted display Helmet mounted displays Biometrics Verification Periocular Region concentricity augmented reality images

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Demographic bias in biometrics: A survey on an emerging challenge

arXiv

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arXiv 2020年

作者： Drozdowski, Pawel Rathgeb, Christian Dantcheva, Antitza Damer, Naser Busch, Christoph Da/sec - Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt Germany Inria Sophia Antipolis France Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Mathematical and Applied Visual Computing Tu Darmstadt Darmstadt Germany

Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g. access control) and non-cooperative (e.g. surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioural characteristics of human beings, which enable for individuals to be reliably recognised 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 labelled as "racist" or "biased" by the media, non-governmental organisations, 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. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

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A Multi-detector Solution Towards an Accurate and Generalized Detection of Face Morphing Attacks

A Multi-detector Solution Towards an Accurate and Generalize...

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International Conference on Information Fusion

作者： Naser Damer Steffen Zienert Yaza Wainakh Alexandra Moseguí Saladié Florian Kirchbuchner Arjan Kuijper Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

Face morphing attack images are built to be verifiable to multiple identities. Associating such images to identity documents leads to building faulty identity links, causing vulnerabilities in security critical processes. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work introduces a multi-detector fusion solution that aims at gaining both, accuracy and generalization over different morphing types. This is performed by fusing classification scores produced by detectors trained on databases with variations in morphing type and image pairing protocols. This work develop and evaluate the proposed solution along with baseline solutions by building a database with three different pairing protocols and two different morphing approaches. This proposed solution successfully lead to decreasing the Bona Fide Presentation Classification Error Rate at 1.0% Attack Presentation Classification Error Rate from 15.7% and 3.0% of the best performing single detector to 2.7% and 0.0%, respectively on two face morphing techniques, pointing out a highly generalized performance.

关键词： Protocols Face Databases Face recognition Detectors Probes Feature extraction

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D-ID-Net: Two-Stage Domain and Identity Learning for Identity-Preserving Image Generation From Semantic Segmentation

D-ID-Net: Two-Stage Domain and Identity Learning for Identit...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Naser Damer 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)9781728150239

ISBN: (纸本)9781728150246

Training functionality-demanding AR/VR systems require accurate and robust gaze estimation and tracking solutions. Achieving such a performance requires the availability of diverse eye image data that might only be acquired by the means of image generation. Works addressing the generation of such images did not target realistic and identity-specific images, nor did they address the practical-relevant case of generation from semantic labels. Therefore, this work proposes a solution to generate realistic and identity-specific images that correspond to semantic labels, given samples of a specific identity. Our proposed solution consists of two stages. In the first stage, a network is trained to transform the semantic label into a corresponding eye image of a generic identity. The second stage is an identity-specific network that induces identity details on the generic eye image. The results of our D-ID-Net solutions shows a high degree of identity-preservation and similarity to the ground-truth images, with an RMSE of 7.235.

关键词： Semantics Training Image segmentation Gallium nitride Image generation Databases Robustness

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Virtual lenses as embodied tools for immersive analytics

arXiv

引用

arXiv 2019年

作者： Kluge, Sven Gladisch, Stefan von Lukas, Uwe Freiherr Staadt, Oliver Tominski, Christian Institute for Visual & Analytic Computing University of Rostock Fraunhofer Institute for Computer Graphics Research IGD Rostock Germany

Interactive lenses are useful tools for supporting the analysis of data in different ways. Most existing lenses are designed for 2D visualization and are operated using standard mouse and keyboard interaction. On the other hand, research on virtual lenses for novel 3D immersive visualization environments is scarce. Our work aims to narrow this gap in the literature. We focus particularly on the interaction with lenses. Inspired by natural interaction with magnifying glasses in the real world, our lenses are designed as graspable tools that can be created and removed as needed, manipulated and parameterized depending on the task, and even combined to flexibly create new views on the data. We implemented our ideas in a system for the visual analysis of 3D sonar data. Informal user feedback from more than a hundred people suggests that the designed lens interaction is easy to use for the task of finding a hidden wreck in sonar data. Copyright © 2019, The Authors. All rights reserved.

关键词： visualization

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