Directional Sensitivity of Gaze-Collinearity Features in Liveness Detection

Ali, Asad; Deravi, Farzin; Hoque, Sanaul

doi:10.1109/est.2013.7

Cited by 17 publications

(9 citation statements)

References 15 publications

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“…Also, relying only on spontaneous facial movements to detect vitality and applying non-rigid motion detectors becomes powerless under video replay attacks and sophisticated mask attacks. In addition to these involuntary signals, also other dynamic cues can be exploited to overcome the above limitations, by explicitly requesting the user to perform some specific movements, using the so-called challenge-response methods [22,[43][44][45].…”

Section: Motion Analysis-based Methodsmentioning

confidence: 99%

Face recognition under spoofing attacks: countermeasures and research directions

Correia

Hadid

2017

IET biom.

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Among tangible threats facing current biometric systems are spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby attempting to gain illegitimate access and advantages. Recently, an increasing attention has been given to this research problem, as can be attested by the growing number of articles and the various competitions that appear in major biometric forums. This study presents a comprehensive overview of the recent advances in face anti-spoofing state-of-the-art, discussing existing methodologies, available benchmarking databases, reported results and, more importantly, the open issues and future research directions. As a case study for illustration, a face anti-spoofing method is described, which employs a colour local binary pattern descriptor to jointly analyse colour and texture available from the luminance and chrominance channels. Two publicly available databases are used for the analysis, and the importance of inter-database evaluation to attest the generalisation capabilities of an anti-spoofing method is discussed.

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Section: Motion Analysis-based Methodsmentioning

confidence: 99%

Face recognition under spoofing attacks: countermeasures and research directions

Correia

Hadid

2017

IET biom.

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“…In these methods, the user is required to respond to a 'challenge' instructed by the system. The system requests the user to perform some specific movements like 'blink right eye' or 'rotate the head clockwise' [22][23][24] or gaze towards a predefined stimulus [25].…”

Section: Challenge-responsementioning

confidence: 99%

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

2020

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Biometric presentation attack detection (PAD) is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face, or iris recognition instead of passwords. In this study, the authors survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, they investigate the effectiveness of fine-tuning very deep convolutional neural networks to the task of face and iris antispoofing. They compare two different fine-tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with a very low error rate. Cross-dataset evaluation on face PAD showed better generalisation than state-of-the-art. They also performed cross-dataset testing on iris PAD datasets in terms of equal error rate, which was not reported in the literature before. Additionally, they propose the use of a single deep network trained to detect both face and iris attacks. They have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, they analysed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.

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“…Few studies focused on this type of method where detection techniques are proposed to analyze the 3D facial structure to distinguish real from fake samples in liveness detection system [74][75][76][77].…”

Section: D Propertiesmentioning

confidence: 99%

Insight on face liveness detection: A systematic literature review

Raheem¹,

Ahmad²,

Adnan³

2019

IJECE

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<p>To review researcher’s attempts in response to the problem of spoofing and liveness detection, mapping the research overview from the literature survey into a suitable taxonomy, exploring the basic properties of the field, motivation of using liveness detection methods in face recognition, and Problems that may restrain the advantages. We presented a subjected search on face recognition with liveness detection and its synonyms in four main databases: Web of science, Science Direct, Scopus and IEEE Xplore. We believe that these databases are widely inclusive enough to cover the literature.<em> </em>The final number of articles considered is 65 articles. 4 of them where review and survey articles that described a general overview about liveness detection and anti-spoofing methods. Since 2012, and despite of leaving some areas unestablished and needs more attention, researchers tried to keep track of liveness detection in several ways. No matter what their category is, articles concentrated on challenges that faces the full utility of anti-spoofing methods and recommended some solutions to overcome these challenges. In this paper, different types of liveness detection and face anti-spoofing techniques are investigated to keep researchers updated with what is being developed in this field.</p>

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Directional Sensitivity of Gaze-Collinearity Features in Liveness Detection

Cited by 17 publications

References 15 publications

Face recognition under spoofing attacks: countermeasures and research directions

Face recognition under spoofing attacks: countermeasures and research directions

Deep convolutional neural networks for face and iris presentation attack detection: survey and case study

Insight on face liveness detection: A systematic literature review

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