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Face liveness detection by exploring multiple scenic clues

机译:通过探索多个风景线索来检测人脸是否活跃

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Liveness detection is an indispensable guarantee for reliable face recognition, which has recently received enormous attention. In this paper we propose three scenic clues, which are non-rigid motion, face-background consistency and imaging banding effect, to conduct accurate and efficient face liveness detection. Non-rigid motion clue indicates the facial motions that a genuine face can exhibit such as blinking, and a low rank matrix decomposition based image alignment approach is designed to extract this non-rigid motion. Face-background consistency clue believes that the motion of face and background has high consistency for fake facial photos while low consistency for genuine faces, and this consistency can serve as an efficient liveness clue which is explored by GMM based motion detection method. Image banding effect reflects the imaging quality defects introduced in the fake face reproduction, which can be detected by wavelet decomposition. By fusing these three clues, we thoroughly explore sufficient clues for liveness detection. The proposed face liveness detection method achieves 100% accuracy on Idiap print-attack database and the best performance on self-collected face anti-spoofing database.
机译:活跃度检测是可靠的人脸识别必不可少的保证,近来受到了极大的关注。本文提出了非刚性运动,人脸背景一致性和成像条纹效果这三个风景名胜的线索,以进行准确,有效的人脸活动度检测。非刚性运动线索指示真实面部可表现出的面部运动(例如眨眼),并且基于低秩矩阵分解的图像对齐方法被设计为提取此非刚性运动。人脸与背景的一致性线索认为,对于假人脸照片,人脸和背景的运动具有较高的一致性,而对于真实人脸则具有较低的一致性,这种一致性可以作为一种有效的活泼线索,这是基于GMM的运动检测方法所探索的。图像条纹效应反映了伪造人脸再现中引入的成像质量缺陷,可以通过小波分解来检测。通过融合这三个线索,我们彻底探索了足够的线索来进行活力检测。所提出的人脸活跃度检测方法在Idiap打印攻击数据库上达到100%的准确性,并在自收集的人脸反欺骗数据库上达到最佳性能。

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