Face recognition with liveness detection using eye and mouth movement

Singh, Avinash Kumar; Joshi, Piyush; Nandi, Gora Chand

doi:10.1109/icspct.2014.6884911

Cited by 59 publications

(26 citation statements)

References 10 publications

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“…A bigger hole could have made the task of gaze direction easier for the attackers but may have exposed other biometric indicators of the attacker (e.g. iris) that would have undermined their spoofing attempt [25]. During a real attempt, the video of the genuine user was recorded and used for subsequent replay attacks.…”

Section: Methodsmentioning

confidence: 99%

Gaze stability for liveness detection

Ali

Hoque

Deravi

2016

Pattern Anal Applic

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Spoofing attacks on biometric systems are one of the major impediments to their use for secure unattended applications. This paper explores features for face liveness detection based on tracking the gaze of the user. In the proposed approach, a visual stimulus is placed on the display screen, at apparently random locations, which the user is required to follow while their gaze is measured. This visual stimulus appears in such a way that it repeatedly directs the gaze of the user to specific positions on the screen. Features extracted from sets of collinear and colocated points are used to estimate the liveness of the user. Data are collected from genuine users tracking the stimulus with natural head/eye movements and impostors holding a photograph, looking through a 2D mask or replaying the video of a genuine user. The choice of stimulus and features are based on the assumption that natural head/eye coordination for directing gaze results in a greater accuracy and thus can be used to effectively differentiate between genuine and spoofing attempts. Tests are performed to assess the effectiveness of the system with these features in isolation as well as in combination with each other using score fusion techniques. The results from the experiments indicate the effectiveness of the proposed gaze-based features in detecting such presentation attacks.

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Section: Methodsmentioning

confidence: 99%

Gaze stability for liveness detection

Ali

Hoque

Deravi

2016

Pattern Anal Applic

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“…The literature on liveness detection has highlighted several other studies with a combination rather than one liveness indicator. These studies were included in our search period are [78][79][80][81][82][83].…”

Section: Other Liveness Indicatorsmentioning

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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“…Experiments showed that the methods were effective in counter spoofing for all three types of attack (photo, 2D mask and video replay). Singh et al [12] proposed a liveness detection scheme based on eye and mouth movements. The challenges were generated randomly such as eye or mouth movement (openness/ closeness).…”

Section: B Active Techniquesmentioning

confidence: 99%