A dual-channel model based on multi-feature fusion for face liveness detection

Abstract

Face liveness detection algorithms are widely used in anti-spoofing applications, which guarantee the accuracy and security of face recognition systems. However, with the continuous development of technologies such as 3D printing and artificial intelligence, traditional face-liveness detection algorithms struggle to withstand spoofing attacks effectively. In this paper, we propose a multi-feature fusion algorithm using only facial video for face liveness detection. Initially, we design a dual-channel network named DC-Net. It can extract robust remote photoplethysmography signals directly from 5-second facial videos, as well as fine global texture features from the keyframes of the image sequence. Subsequently, a fusion module based on the attention mechanism is used to carry out feature-level fusion. Ultimately, we use the fully connected layer for binary classification. Our methodology was validated using the REPLAY-ATTACK dataset and the 3DMAD dataset, demonstrating that for printing attacks, screen replay attacks, and 3D mask attacks, our approach attained an accuracy of 99.79% and 100% on both datasets, respectively. Meanwhile, cross-dataset testing was conducted on the CASIA-FASD and HKBU-MARs V1+ datasets, achieving HTER of 25.56% and 0.00%, respectively. This indicates that the algorithm has good accuracy and robustness in dealing with spoofing attacks in many different scenarios, which provides important ideas and technical support for the design and implementation of reliable face recognition systems.