The 2nd competition on counter measures to 2D face spoofing attacks

Chingovska, Ivana; Yang, Jian; Lei, Zhen; Dong, Yi; Li, S. Z.; Kahm, Olga; Gläser, Christian; Damer, Naser; Kuijper, Arjan; Nouak, Alexander; Komulainen, Jukka; Pereira, Tiago de Freitas; Gupta, Shubham; Khandelwal, Sahaj; Bansal, Srishti; Rai, Atul; Krishna, Tarun; Goyal, Dushyant; Waris, Muhammad Adeel; Zhang, Honglei; Ahmad, Iftikhar; Kıranyaz, Serkan; Gabbouj, Moncef; Tronci, Roberto; Pili, Maurizio; Sirena, N.; Roli, Fabio; Galbally, Javier; Ficrrcz, J.; Pinto, Allan; Pedrini, Hélio; Schwartz, William Robson; Rocha, Anderson; Anjos, André; Marcel, Sébastien

doi:10.1109/icb.2013.6613026

Cited by 82 publications

(103 citation statements)

References 22 publications

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“…LBP texture descriptors have been successfully used for face-PAD [20]. Here we propose a new texture-based approach for face-PAD, using Gabor-jets.…”

Section: B Face-pad Using Gabor-jetsmentioning

confidence: 99%

The Replay-Mobile Face Presentation-Attack Database

Costa‐Pazo

Bhattacharjee

Vázquez-Fernández

et al. 2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

148

109

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Abstract-For face authentication to become widespread on mobile devices, robust countermeasures must be developed for face presentation-attack detection (PAD). Existing databases for evaluating face-PAD methods do not capture the specific characteristics of mobile devices. We introduce a new database, REPLAY-MOBILE, for this purpose.1 This publicly available database includes 1, 200 videos corresponding to 40 clients. Besides the genuine videos, the database contains a variety of presentation-attacks. The database also provides three nonoverlapping sets for training, validating and testing classifiers for the face-PAD problem. This will help researchers in comparing new approaches to existing algorithms in a standardized fashion. For this purpose, we also provide baseline results with stateof-the-art approaches based on image quality analysis and face texture analysis 2 .

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“…LBP texture descriptors have been successfully used for face-PAD [20]. Here we propose a new texture-based approach for face-PAD, using Gabor-jets.…”

Section: B Face-pad Using Gabor-jetsmentioning

confidence: 99%

The Replay-Mobile Face Presentation-Attack Database

Costa‐Pazo

Bhattacharjee

Vázquez-Fernández

et al. 2016

2016 International Conference of the Biometrics Special Interest Group (BIOSIG)

148

109

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“…[14], [15], [16]. Spoofing attacks are, however, varying and unpredictable in nature and thus it is difficult to predict how countermeasures will generalise to spoofing attacks 'in the wild', where their true nature can never be known with certainty.…”

Section: Generalizationmentioning

confidence: 99%

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

Hadid

Evans²,

Marcel

et al. 2015

IEEE Signal Process. Mag.

188

103

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Biometrics already form a significant component of current and emerging identification technologies.Biometrics systems aim to determine or verify the identity of an individual from their behavioural and/or biological characteristics. Despite significant progress, some biometric systems fail to meet the multitude of stringent security and robustness requirements to support their deployment in some practical scenarios.Among current concerns are vulnerabilities to spoofing, namely persons who masquerade as others in order to gain illegitimate accesses to protected data, services or facilities. While the study of spoofing, or rather anti-spoofing, has attracted growing interest in recent years, the problem is far from being solved and will require far greater attention in coming years. This tutorial article presents an introduction to spoofing and anti-spoofing research. It describes the vulnerabilities, presents an evaluation methodology for the assessment of spoofing and countermeasures, and outlines research priorities for the future.

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“…The best results are achieved if the fused methods use complementary features which discern spoofing attacks from different aspects [29]. Such methods achieve the best performances up to date, even when confronted with a multitude of attack types [30].…”

Section: Related Workmentioning

confidence: 99%

“…It does not intend to compete with the best performing methods like in [30], which rely on complex combinations of anti-spoofing features. Instead, it aims to demonstrate how client-specific classification can be beneficial even when using simple features, upon which many of the best performing methods are built.…”

Section: Client-specific Approachesmentioning

confidence: 99%

On the Use of Client Identity Information for Face Antispoofing

Chingovska

Anjos

2015

IEEE Trans.Inform.Forensic Secur.

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With biometrics playing the role of a password which can not be replaced if stolen, the necessity of establishing countermeasures to biometric spoofing attacks has been recognized. Regardless of the biometric mode, the typical approach of anti-spoofing systems is to classify biometric evidence based on features discriminating between real accesses and spoofing attacks. For the first time, to the best of our knowledge, this paper studies the amount of client-specific information within these features and how it affects the performance of anti-spoofing systems. We make use of this information to build two client-specific anti-spoofing solutions, one relying on a generative and another one on a discriminative paradigm. The proposed methods, tested on a set of state-of-the-art antispoofing features for the face mode, outperform the clientindependent approaches with up to 50% relative improvement and exhibit better generalization capabilities on unseen types of spoofing attacks.

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The 2nd competition on counter measures to 2D face spoofing attacks

Cited by 82 publications

References 22 publications

The Replay-Mobile Face Presentation-Attack Database

The Replay-Mobile Face Presentation-Attack Database

Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned

On the Use of Client Identity Information for Face Antispoofing

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