Eigen-PEP for video face recognition

Abstract

To effectively solve the problem of large scale video face recognition, we argue for a comprehensive, compact, and yet flexible representation of a face subject. It shall comprehensively integrate the visual information from all relevant video frames of the subject in a compact form. It shall also be flexible to be incrementally updated, incorporating new or retiring obsolete observations. In search for such a representation, we present the Eigen-PEP that is built upon the recent success of the probabilistic elastic part (PEP) model. It first integrates the information from relevant video sources by a part-based average pooling through the PEP model, which produces an intermediate high dimensional, partbased, and pose-invariant representation. We then compress the intermediate representation through principal component analysis, and only a number of principal eigen dimensions are kept (as small as 100). We evaluate the Eigen-PEP representation both for video-based face verification and identification on the YouTube Faces Dataset and a new Celebrity- 1000 video face dataset, respectively. On YouTube Faces, we further improve the state-of-the-art recognition accuracy. On Celebrity-1000, we lead the competing baselines by a significant margin while offering a scalable solution that is linear with respect to the number of subjects.