Face live detection method based on physiological motion analysis

Wang, Liting; Ding, Xiaoqing; Fang, Chi

doi:10.1016/s1007-0214(09)70135-x

Cited by 54 publications

(20 citation statements)

References 12 publications

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“…Movement of the eye based analysis was introduced for embedded face recognition system [16], which takes variation of each eye-region of input images into account to differentiate between live images and non-live or fake images. A similar approach was taken by Liting, et al [17] for liveness detection, which is based on physiological motion detected by estimating eye blinks from a video sequence and an eye contour extraction algorithm. Spoofing attacks on a security system can be done mostly by using a print image, a photograph image, or a video frame [18] which may be combated only by detecting the liveness of these media.…”

Section: Discussionmentioning

confidence: 99%

Efficient live face detection to counter spoof attack in face recognition systems

Biswas

Alam

2015

SPIE Proceedings

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Face recognition is a critical tool used in almost all major biometrics based security systems. But recognition, authentication and liveness detection of the face of an actual user is a major challenge because an imposter or a non-live face of the actual user can be used to spoof the security system. In this paper, a robust technique is proposed which detects liveness of faces in order to counter spoof attacks. The proposed technique uses a three-dimensional (3D) fast Fourier transform (FFT) to compare spectral energies of a live face and a fake face in a mathematically selective manner. The mathematical model involves evaluation of energies of selective high frequency bands of average power spectra of both live and non-live faces. It also carries out proper recognition and authentication of the face of the actual user using the fringe-adjusted joint transform correlation technique, which has been found to yield the highest correlation output for a match. Experimental tests using real life datasets show that the proposed technique yields excellent results for identifying live faces.

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Section: Discussionmentioning

confidence: 99%

Efficient live face detection to counter spoof attack in face recognition systems

Biswas

Alam

2015

SPIE Proceedings

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“…Face anti-spoofing has been studied for decades. Some previous works [36,43,25,1] attempt to detect the evidence of liveness (i.e., eye-blinking). Another works are based on contextual [37,26] and moving [44,13,22] information.…”

Section: Methodsmentioning

confidence: 99%

A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

Zhang

Wang

Liu

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

149

158

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Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face antispoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤ 170) and modalities (≤ 2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1, 000 subjects with 21, 000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https

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“…The centres of both eyes in the facial image are located, and if the variance of each eye region is larger than a preset threshold, the image is considered to be live, and if not, the image is classified as a photographic artefact. Wang et al [6] presented a liveness detection method in which physiological motion is detected by estimating the eye blink with an eye contour extraction algorithm. They use active shape models with a random forest classifier trained to recognize the local appearance around each landmark.…”

Section: Passive Techniquesmentioning

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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Face live detection method based on physiological motion analysis

Cited by 54 publications

References 12 publications

Efficient live face detection to counter spoof attack in face recognition systems

Efficient live face detection to counter spoof attack in face recognition systems

A Dataset and Benchmark for Large-Scale Multi-Modal Face Anti-Spoofing

Gaze stability for liveness detection

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