Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection
This addresses security vulnerabilities in facial recognition systems against spoofing attacks, representing an incremental improvement in detection methods.
The paper tackles face spoofing attacks in biometric security by proposing an anisotropic diffusion-based kernel matrix model (ADKMM) for face liveness detection, which outperforms state-of-the-art methods on two public datasets.
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.