Liveness Detection Using Gaze Collinearity

Ali, Asad; Deravi, Farzin; Hoque, Sanaul

doi:10.1109/est.2012.12

Cited by 37 publications

(16 citation statements)

References 9 publications

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“…In order to spoof the system the attacker may hold the photograph still (without moving the photograph in response to the stimuli) to generate near-perfect collinearity and colocation features. However, such an attack is easily detected by measuring the overall spread of landmark locations in the captured images during the entire presentation session, R, and check that this value is above a certain threshold to detect such presentation attacks [2]. In fact, two thresholds are used in the operation of the proposed system.…”

Section: Colocation Featuresmentioning

confidence: 99%

“…This paper provides a unified and formal framework for bringing together the authors' previous work that dealt with features based on gaze stability [2,3]. The novel contributions of this work include a mathematical generalization of the originally proposed features to incorporate more complex stimulus trajectories used as a challenge and extension of the experimental work to include more test subjects and presentation attack scenarios.…”

Section: Introductionmentioning

confidence: 99%

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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: Colocation Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaze stability for liveness detection

Ali

Hoque

Deravi

2016

Pattern Anal Applic

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“…Others proposed video-based anti-spoofing methods [15], [14], [35] for biometric authentication. Instead, we propose to use the previously unexplored combination of video and accelerometer data to verify the liveness of the video capture process.…”

Section: Introductionmentioning

confidence: 99%

Seeing is not believing

Rahman

Topkara

Cărbunar

2013

Proceedings of the 29th Annual Computer Security Applications Conference

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The visual information captured with camera-equipped mobile devices has greatly appreciated in value and importance as a result of their ubiquitous and connected nature. Today, banking customers expect to be able to deposit checks using mobile devices, and broadcasting videos from camera phones uploaded by unknown users is admissible on news networks. We present Movee, a system that addresses the fundamental question of whether the visual stream coming into a mobile app from the camera of the device can be trusted to be untampered with, live data, before it can be used for a variety of purposes.Movee is a novel approach to video liveness analysis for mobile devices. It is based on measuring the consistency between the data from the accelerometer sensor and the inferred motion from the captured video. Contrary to existing algorithms, Movee has the unique strength of not depending on the audio track. Our experiments on real user data have shown that Movee achieves 8% Equal Error Rate.

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“…A typical image‐based face anti‐spoofing system includes the following three steps: user behavior modeling, user cooperation, and data‐driven characterization. Early anti‐spoofing systems always request user cooperation, such as shaking one's head or speaking phrases, which is inconvenient and highly relies on users' cooperation. Therefore, other works shift the attention to develop non‐intrusive methods .…”

Section: Introductionmentioning

confidence: 99%

Cover patches: A general feature extraction strategy for spoofing detection

Cai

Leng

et al. 2018

Concurrency and Computation

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Summary Face anti‐spoofing has attracted many attentions in security applications, such as mobile payment and entrance guard. Until now, face anti‐spoofing technique is still a challenging task. Mainstream image‐based spoofing algorithms usually use global motion or texture information to distinguish whether an input face is live or fake. However, the performance of these methods are sensitive in light changes, or images acquired from different sensors. The main reason is that spoofed face image always has slight different texture in local areas, such as landmark or salient region of face. To this end, this paper proposes a novel multi‐patches feature extraction strategy to detect spoofing. First, a set of patches with specific combination scheme is selected to cover the face image. Second, features such as hand‐crafted Gray Level Co‐occurrence Matrix (GLCM), Local Binary Patterns (LBP), or deep features are extracted from these patches. Third, all features are combined as the global descriptor of the face image, then fed into an SVM classifier to verify the anti‐spoofing detection. Experimental results show that the proposed strategy can effectively enhance the performance, concerning with the accuracy of spoofed face detection in four widely used anti‐spoofing databases.

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Liveness Detection Using Gaze Collinearity

Cited by 37 publications

References 9 publications

Gaze stability for liveness detection

Gaze stability for liveness detection

Seeing is not believing

Cover patches: A general feature extraction strategy for spoofing detection

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