Multi-Image and Video-Based Face Recognition

Face recognition is an important problem in computer vision with applications ranging from surveillance to robotics to the organization of personal image collections. In this work, we focus on unconstrained face recognition in videos. The setup consists of a set of gallery videos (or images) for each of the identities. During testing, an incoming probe video (or multiple images) is compared to the gallery to determine the most likely match. This area has not received much attention in the past. We first investigate the simplified version of temporally unrelated multiple images, where we explore the representational power of subspace-based distance functions. We create new powerful combinations of low-level features and subspace measures.