FaceLivePlus: A Unified System for Face Liveness Detection and Face Verification

Face verification is a trending way to verify someone's identity in broad applications. But such systems are vulnerable to face spoofing attacks via, for example, a fraudulent copy of a photo, making it necessary to include face liveness detection as an additional safeguard. Among most existing studies, the face liveness detection is realized in a separate machine learning model in addition to the model for face verification. Such a two-model configuration may face challenges when deployed onto platforms with limited computation power and storage (e.g. mobile phone, IoT devices), especially considering each model may have millions of parameters. Inspired by the fact that humans can verify a person's identity and liveness at a single glance from a face, we develop a novel system, named FaceLivePlus, to learn a single and universal face descriptor for the two tasks (face verification and liveness detection) so that the computational workload and storage space can be halved. To achieve this, we formulate the underlying relationship between the two tasks, and seamlessly embed this relationship in a distance ranking deep model. The model directly works on features rather than classification labels, which makes the system well generalized on unseen data. Extensive experiments show that our average half total error rate (HTER) has at least 15% and 8% improvement from the state-of-the-arts on two benchmark datasets. We anticipate this approach could become a new direction for face authentication.