Multi-Level Feature Perception Network for Set-Based Video Face Identification

As an important biometric technology, video face identification has broad application prospects and has been widely concerned by researchers for a long time. Although many video face identification methods have emerged, they still have not fully overcome the inherent problems such as intra-video diversity, inter-video similarity. In view of this, a set-based video face identification method called Multi-level Feature Perception Network (MFPN) is proposed, which consists of two modules: a video feature generation module and a distance metric learning module. These modules fully consider the cohesiveness within each video and the similarity between different videos. At first, the video feature generation module is used to learn (or perceive) the atom-level features of each frame, and in this process, the cohesiveness problem within each video is considered to facilitate the subsequent learning of compact video features. Then, the distance metric learning module is utilized to further learn (or perceive) the deep concept-level features of videos taking into account the similarity between different videos to enhance the discriminative ability of these features. Finally, the class of the test video is determined based on its deep concept-level features. Experimental results on the Honda, MoBo, YTC, and YTF video face datasets show that the proposed method significantly improves recognition rates in both video face identification and video face verification tasks.