Integration of image quality and motion cues for face anti-spoofing: A neural network approach

• Published in: Journal of visual communication and image representation Vol. 38; pp. 451 - 460
• Main Authors: Feng, Litong, Po, Lai-Man, Li, Yuming, Xu, Xuyuan, Yuan, Fang, Cheung, Terence Chun-Ho, Cheung, Kwok-Wai
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2016
• Subjects: Cues; Dense optical flow; Errors; Face anti-spoofing; Feature fusion; Image quality; Neural network; Neural networks; Shearlet; Spoofing; Strategy; Feature fusion; Neural network; Face anti-spoofing; Shearlet; Dense optical flow
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2016.03.019

•A multi-cues integration framework is proposed using a hierarchical neural network.•Bottleneck representations are effective in multi-cues feature fusion.•Shearlet is utilized to perform face image quality assessment.•Motion-based face liveness features are automatically learned using autoencoders. Many trait-specific countermeasures to face spoofing attacks have been developed for security of face authentication. However, there is no superior face anti-spoofing technique to deal with every kind of spoofing attack in varying scenarios. In order to improve the generalization ability of face anti-spoofing approaches, an extendable multi-cues integration framework for face anti-spoofing using a hierarchical neural network is proposed, which can fuse image quality cues and motion cues for liveness detection. Shearlet is utilized to develop an image quality-based liveness feature. Dense optical flow is utilized to extract motion-based liveness features. A bottleneck feature fusion strategy can integrate different liveness features effectively. The proposed approach was evaluated on three public face anti-spoofing databases. A half total error rate (HTER) of 0% and an equal error rate (EER) of 0% were achieved on both REPLAY-ATTACK database and 3D-MAD database. An EER of 5.83% was achieved on CASIA-FASD database.