TDS-Net: Towards Fast Dynamic Random Hand Gesture Authentication via Temporal Difference Symbiotic Neural Network

Hand gesture is a new emerging biometric trait containing both physiological and behavioral characteristics. With the popularity of various cameras, and the rich identity features and contactless authentication mode embedded in gestures themselves, vision-based hand gesture authentication has great potential value. However, current hand gesture authentication methods heavily rely on defined gestures and require identical enrollment and verification gestures, which limits the user-friendliness and efficiency of authentication. It is arguably true that authentication in a simpler and faster way, without the need to remember gestures, will be more approachable. Thus, a fast dynamic random hand gesture authentication method is introduced, in which users can perform a random improvised gesture in both the enrollment and verification stage. To better utilize the physiological and behavioral characteristics of hand gestures, an efficient network named Temporal Difference Symbiotic Neural Network (TDS-Net) equipped with our designed behavioral energy-based feature fusion module (BE-Fusion module) is proposed. Extensive experiments on the SCUT-DHGA dataset demonstrate that TDS-Net outperforms the recent state-of-the-art methods.