Integration of image quality and motion cues for face anti spoofing: A neural network approach
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.
J. Vis. Commun. Image Represent.