Multiscale Super-Images for Dynamic Hand Gesture Authentication
Zenan Lin, Wenwei Song, Wenxiong Kang · IEEE Transactions on Circuits and Systems for Video Technology · 2025
The dynamic hand gesture is an emerging biometric trait that has attracted the attention of researchers due to its rich physiological and behavioral characteristics. The previous studies primarily focused on extracting and utilizing the physiological characteristics, while ignoring the rich behavioral characteristics contained in hand gesture movements. The dynamic hand gesture authentication performance will be improved if behavioral characteristics can be effectively extracted and fused with physiological characteristics for authentication. In addition, existing methods still suffer from insufficient feature extraction capabilities and low efficiency in extracting behavioral characteristics from complex dynamic hand gestures. To address these issues, this paper first proposes multiscale dynamic hand gesture (MDHG) super-images to represent the behavioral characteristics of hand gestures, containing sufficient local and global motion cues. Furthermore, for the super-images, this paper proposes a two-stream network consisting of a spatiotemporal feature extraction backbone and an identity-aggregation module to fully extract and fuse the physiological and behavioral characteristics of hand gestures, which significantly improves the accuracy of dynamic hand gesture authentication. Extensive experiments on two benchmark datasets, SCUT-DHGA and HandLogin, show that our method achieves superior performance with fewer parameters and FLOPs than other networks, validating the effectiveness, generalizability, and security of our proposed method.