Jointly learning dictionaries and subspace structure for video-based face recognition

Abstract

In video-sharing websites and surveillance scenarios, there are often a large amount of face videos. This paper proposes a joint dictionary learning and subspace segmentation method for video-based face recognition (VFR). We assume that the face images from one subject video lie in a union of multiple linear subspaces, and there exists a global dictionary to represent these images and segment them to their corresponding subspaces. This assumption results in a “chicken and egg” problem, where subspace clustering and dictionary learning are mutually dependent. To solve thiss problem, we propose a joint optimization model that includes three parts. The first part seeks a low-rank representation for subspace segmentation; the second part encourages the dictionary to accurately represent the data while tolerating frame-wise corruption or outliers; and the third part is a regularization on the dictionary. An alternating minimization method is employed as an efficient solution to the proposed joint formulation. In each iteration, it alternately learns the subspace structure and the dictionary by improving the learning results. Experiments on three video-based face databases show that our approach consistently outperforms the state-of-the-art methods.