2014/04/22 by Jim Jing-Yan Wang, Wang, Jim Jing-Yan, Majed Alzahrani +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1404.5588
arxiv created 2014/04/22 · openalex publication_date 2014/04/22 · arxiv updated 2014/04/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets. The margin of an image set is defined as the difference of the distance to its nearest image set from different classes and the distance to its nearest image set of the same class. By modeling the image sets by using both their image samples and their affine hull models, and maximizing the margins of the images sets, the image set representation parameter learning problem is formulated as an minimization problem, which is further optimized by an expectation -maximization (EM) strategy with accelerated proximal gradient (APG) optimization in an iterative algorithm. To classify a given test image set, we assign it to the class which could provide the largest margin. Experiments on two applications of video-sequence-based face recognition demonstrate that the proposed method significantly outperforms state-of-the-art image set classification methods in terms of both effectiveness and efficiency.