Depthwise Temporal Non-Local Network for Faster and Better Dynamic Hand Gesture Authentication

Dynamic hand gesture is an emerging and promising biometric trait. It contains both physiological and behavioral characteristics, which on the one hand can theoretically make authentication systems more accurate and more secure, and on the other hand can increase the difficulty of model design because it is essentially a fine-grained video understanding task. For authentication systems, equal error rate (EER) and real-time performance are two vital metrics. Current video understanding-based hand gesture authentication methods mainly focus on lowering the EER while neglecting to reduce the computational cost. In this paper, we propose a 2D CNN-based depthwise temporal non-local network (DwTNL-Net) that can take into account both EER and running efficiency. To enable the DwTNL-Net with spatiotemporal information processing capability, we design a temporal sharpening (TS) module and a DwTNL module for short- and long-term identity feature modeling, respectively. The TS module can assist the backbone in local behavioral characteristic understanding and can simultaneously reduce redundant information and highlight behavioral cues while retaining sufficient physiological characteristics. In contrast, the DwTNL module focuses on summarizing global information and discovering stable patterns, which are finally used for local information enhancement. The complementary combination of our TS and DwTNL modules makes DwTNL-Net achieve substantial performance improvements. Extensive experiments on the SCUT-DHGA dataset and sufficient statistical analyses fully demonstrate the superiority and efficiency of our DwTNL-Net. The code is available at
https://github.com/SCUT-BIP-Lab/DwTNL-Net
.