检索结果-内蒙古大学图书馆

Compact Models for Periocular Verification Through Knowledge Distillation

学校读者我要写书评

暂无评论

Compact Models for Periocular Verification Through Knowledge...

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

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

学校读者我要写书评

暂无评论

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

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

关键词： Face recognition

Demographic bias in presentation attack detection of iris recognition systems

学校读者我要写书评

暂无评论

arXiv 2020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 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

Micro Stripes Analyses for Iris Presentation Attack Detection

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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

关键词： Faces Face recognition Databases Lighting Probes Videos Collaboration

Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts 40th

学校读者我要写书评

暂无评论

Detecting Face Morphing Attacks by Analyzing the Directed Di...

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

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

学校读者我要写书评

暂无评论

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

Frank-Wolfe-type methods for a class of nonconvex inequality-constrained problems

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zeng, Liaoyuan Zhang, Yongle Li, Guoyin Pong, Ting Kei Wang, Xiaozhou College of Science Zhejiang University of Technology Zhejiang Hangzhou China School of Mathematical Sciences Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Sichuan Chengdu China Department of Applied Mathematics University of New South Wales Sydney Australia Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong

The Frank-Wolfe (FW) method, which implements efficient linear oracles that minimize linear approximations of the objective function over a fixed compact convex set, has recently received much attention in the optimization and machine learning literature. In this paper, we propose a new FW-type method for minimizing a smooth function over a compact set defined as the level set of a single difference-of-convex function, based on new generalized linear-optimization oracles (LO). We show that these LOs can be computed efficiently with closed-form solutions in some important optimization models that arise in compressed sensing and machine learning. In addition, under a mild strict feasibility condition, we establish the subsequential convergence of our nonconvex FW-type method. Since the feasible region of our generalized LO typically changes from iteration to iteration, our convergence analysis is completely different from those existing works in the literature on FW-type methods that deal with fixed feasible regions among subproblems. Finally, motivated by the away steps for accelerating FW-type methods for convex problems, we further design an away-step oracle to supplement our nonconvex FW-type method, and establish subsequential convergence of this variant. Numerical results on the matrix completion problem with standard datasets are presented to demonstrate the efficiency of the proposed FW-type method and its away-step variant. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Exploring the Channels of Multiple Color Spaces for Age and Gender Estimation from Face Images

学校读者我要写书评

暂无评论

Exploring the Channels of Multiple Color Spaces for Age and ...

International Conference on Information Fusion

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

ISBN: (数字)9780996452786

ISBN: (纸本)9781728118406

Soft biometrics identify certain traits of individuals based on their sampled biometric characteristics. The automatic identification of traits like age and gender provides valuable information in applications ranging from forensics to service personalization. Color images are stored within a color space containing different channels. Each channel represents a different portion of the information contained in the image, including these of soft biometric patterns. The analysis of the age and gender information in the different channels and different color spaces was not previously studied. This work discusses the soft biometric performances using these channels and analyzes the sample error overlap between all possible channels to successfully prove that different information is considered in the decision making from each channel. We also present a multi-channel selection protocols and fusion solution of the selected channels. Beside the analyzes of color spaces and their channels, our proposed multi-channel fusion solution extends beyond state-of-the-art performance in age estimation on the widely used Adience dataset.

关键词： Image color analysis Estimation Face Biometrics (access control) Feature extraction Task analysis Channel estimation