Biometrics: In Search of Identity and Security (Q &amp; A)

Akhtar, Zahid; Hadid, Abdenour; Nixon, Mark S.; Tistarelli, Mássimo; Dugelay, Jean‐Luc; Marcel, Sébastien

doi:10.1109/mmul.2017.265090754

Cited by 15 publications

(17 citation statements)

References 19 publications

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“…Note that the performances reported here are similar to some state-of-the-art results reported using the LFW+ 4 . For example, in [16] they reported mean average error of 7.8% for age and 94% and 90% of accuracy for gender and ethnicity, respectively.…”

Section: B Automatic Estimationsupporting

confidence: 86%

“…Soft biometrics refer to physical and behavioral traits that can be semantically described by humans [9], [19]. Although soft biometric traits may not possess sufficient distinctiveness or uniqueness to allow highly accurate recognition [4], they can be useful to enhance person recognition under certain conditions. For instance, Dantcheva et al [10] suggested that it might be possible to perform recognition when considering a sufficient number of them, in a bag of soft biometrics.…”

Section: Introductionmentioning

confidence: 99%

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Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

Gonzalez-Sosa

Fiérrez

Vera-Rodríguez

et al. 2018

IEEE Trans.Inform.Forensic Secur.

111

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The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard and moustache. We consider two assumptions: i) manual estimation of soft biometrics, and ii) automatic estimation from two Commercial Off-The-Shelf systems (COTS). All experiments are reported using the LFW database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores.

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Section: B Automatic Estimationsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

Gonzalez-Sosa

Fiérrez

Vera-Rodríguez

et al. 2018

IEEE Trans.Inform.Forensic Secur.

111

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“…In other words, each FR system in this study is highly effective at detecting ZEI attacks, but is less than 10 times as effective in detecting PAs based on custom silicone masks. FR systems with IAPMR to FMR ratio > 10 are graded as highly vulnerable [3].…”

Section: Discussionmentioning

confidence: 99%

Spoofing Deep Face Recognition with Custom Silicone Masks

Bhattacharjee

Mohammadi

Marcel

2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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We investigate the vulnerability of convolutional neural network (CNN) based face-recognition (FR) systems to presentation attacks (PA) performed using custom-made silicone masks. Previous works have studied the vulnerability of CNN-FR systems to 2D PAs such as print-attacks, or digitalvideo replay attacks, and to rigid 3D masks. This is the first study to consider PAs performed using custom-made flexible silicone masks. Before embarking on research on detecting a new variety of PA, it is important to estimate the seriousness of the threat posed by the type of PA. In this work we demonstrate that PAs using custom silicone masks do pose a serious threat to state-of-the-art FR systems. Using a new dataset based on six custom silicone masks, we show that the vulnerability of each FR system in this study is at least 10 times higher than its false match rate. We also propose a simple but effective presentation attack detection method, based on a low-cost thermal camera.

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“…In the literature, most common biometric modalities adopted are fingerprint and face [3], which are also considered in this work. The fingerprint matching algorithms can be categorised into three categories [1]:…”

Section: Biometric Modality Analysis (Matching)mentioning

confidence: 99%

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

Herbadji¹,

Guermat²,

Ziet

et al. 2020

IET biom.

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Biometrics is now being principally employed in many daily applications ranging from the border crossing to mobile user authentication. In the high-security scenarios, biometrics require stringent accuracy and performance criteria. Towards this aim, multi-biometric systems that fuse the evidences from multiple sources of biometric have exhibited to diminish the error rates and alleviate inherent frailties of the individual biometric systems. In this article, a novel scheme for score-level fusion based on weighted quasi-arithmetic mean (WQAM) has been proposed. Specifically, WQAMs are estimated via different trigonometric functions. The proposed fusion scheme encompasses properties of both weighted mean and quasi-arithmetic mean. Moreover, it does not require any leaning process. Experimental results on three publicly available data sets (i.e. NIST-BSSR1 Multimodal, NIST-BSSR1 Fingerprint and NIST-BSSR1 Face) for multi-modal, multi-unit and multi-algorithm systems show that presented WQAM fusion algorithm outperforms the previously proposed score fusion rules based on transformation (e.g. t-norms), classification (e.g. support vector machines) and density estimation (e.g. likelihood ratio) methods.

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Biometrics: In Search of Identity and Security (Q & A)

Cited by 15 publications

References 19 publications

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

Spoofing Deep Face Recognition with Custom Silicone Masks

Weighted quasi‐arithmetic mean based score level fusion for multi‐biometric systems

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