Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)
This addresses the problem of efficient and accurate face recognition in videos for security and surveillance applications, representing an incremental improvement over existing methods.
The paper tackles video face recognition by proposing a component-wise feature aggregation network (C-FAN) that aggregates features from multiple face images into a single 512-dimensional vector, achieving state-of-the-art performance on benchmark datasets like YouTube Faces, IJB-A, and IJB-S.
We propose a new approach to video face recognition. Our component-wise feature aggregation network (C-FAN) accepts a set of face images of a subject as an input, and outputs a single feature vector as the face representation of the set for the recognition task. The whole network is trained in two steps: (i) train a base CNN for still image face recognition; (ii) add an aggregation module to the base network to learn the quality value for each feature component, which adaptively aggregates deep feature vectors into a single vector to represent the face in a video. C-FAN automatically learns to retain salient face features with high quality scores while suppressing features with low quality scores. The experimental results on three benchmark datasets, YouTube Faces, IJB-A, and IJB-S show that the proposed C-FAN network is capable of generating a compact feature vector with 512 dimensions for a video sequence by efficiently aggregating feature vectors of all the video frames to achieve state of the art performance.