Geometry Guided Feature Aggregation in Video Face Recognition

Baoyun Peng, Xiao Jin, Yichao Wu, Dongsheng Li

Abstract: Video-based face recognition has attracted a significant amount of research interest in both academia and industry due to its wide applications such as surveillance and security. Different from image-based face recognition, abundant information, extracted from a series of frames in a video, would contribute a lot to successful recognition. In other words, the key to improving video face recognition capability is aggregating and integrating profuse information within a video. Existing methods of feature aggregation across frames narrowly focus on the importance of a single frame, while ignoring the geometric relationship among frames in feature space. In this work, we present a geometry-based feature aggregation method rather than a better recognition model. It considers not only the importance of each frame but also the geometric relationship among frames in feature space, which yields more distinguishing video-level representation. Extensive evaluations on IJB-A and YTF datasets indicate that the proposed aggregation method considerably outperforms other feature aggregation methods.