Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets

• Main Authors: Huang, Likun, Lu, Jiwen, Tan, Yap Peng
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2016
• Subjects: Spectral clustering; Multi-view; Set-based face recognition; Co-learning
• Online Access: https://hdl.handle.net/10356/81735; http://hdl.handle.net/10220/39670
• Institution: Nanyang Technological University

Different from the existing approaches that usually utilize single view information of image sets to recognize persons, multi-view information of image sets is exploited in this paper, where a novel method called Co-Learned Multi-View Spectral Clustering (CMSC) is proposed to recognize faces based on image sets. In order to make sure that a data point under different views is assigned to the same cluster, we propose an objective function that optimizes the approximations of the cluster indicator vectors for each view and meanwhile maximizes the correlations among different views. Instead of using an iterative method, we relax the constraints such that the objective function can be solved immediately. Experiments are conducted to demonstrate the efficiency and accuracy of the proposed CMSC method.