  Facial recognition and verification is a widely used biometric technology in
security system. Unfortunately, face biometrics is vulnerable to spoofing
attacks using photographs or videos. In this paper, we present an anisotropic
diffusion-based kernel matrix model (ADKMM) for face liveness detection to
prevent face spoofing attacks. We use the anisotropic diffusion to enhance the
edges and boundary locations of a face image, and the kernel matrix model to
extract face image features which we call the diffusion-kernel (D-K) features.
The D-K features reflect the inner correlation of the face image sequence. We
introduce convolution neural networks to extract the deep features, and then,
employ a generalized multiple kernel learning method to fuse the D-K features
and the deep features to achieve better performance. Our experimental
evaluation on the two publicly available datasets shows that the proposed
method outperforms the state-of-art face liveness detection methods.
