LiveFace: A Multi-task CNN for Fast Face-Authentication

Xiaowen Ying, Xin Li, Mooi Choo Chuah

Abstract: Modern face recognition systems are accurate but they are vulnerable to different types of spoofing attacks. To solve this problem, conventional face authentication systems typically employ an additional module to analyze the liveness of the input faces before feeding it into the face recognition module. Such two-stage designs not only suffer from longer processing time but also require more storage and resources, which are usually limited on mobile and embedded platforms. In this paper, we propose a multi-task Convolutional Neural Network(CNN), namely LiveFace, for face-authentication. Given an input face image, LiveFace generates two outputs through a single stage: (i) a face representation that can be used for identification or verification, and (ii) the corresponding liveness score. The two tasks share lower layers to reduce the computation cost. Experimental results using three datasets show that our model achieves a comparable performance on both face recognition and anti-spoofing tasks but much faster than conventional authentication systems. In addition, we have implemented a prototype of our scheme on Android phones and demonstrated that our scheme can run in real-time on three Android devices that we have tested.