A Video Face Recognition Leveraging Temporal Information Based on Vision Transformer

Video face recognition (VFR) has gained significant attention as a promising field combining computer vision and artificial intelligence, revolutionizing identity authentication and verification. Unlike traditional image-based methods, VFR leverages the temporal dimension of video footage to extract comprehensive and accurate facial information. However, VFR heavily relies on robust computing power and advanced noise processing capabilities to ensure optimal recognition performance. This paper introduces a novel length-adaptive VFR framework based on a recurrent-mechanism-driven Vision Transformer, termed TempoViT. TempoViT efficiently captures spatial and temporal information from face videos, enabling accurate and reliable face recognition while mitigating the high GPU memory requirements associated with video processing. By leveraging the reuse of hidden states from previous frames, the framework establishes recurring links between frames, allowing the modeling of long-term dependencies. Experimental results validate the effectiveness of TempoViT, demonstrating its state-of-the-art performance in video face recognition tasks on benchmark datasets including iQIYI-ViD, YTF, IJB-C, and Honda/UCSD.