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Title: Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets
Authors: Huang, Likun
Lu, Jiwen
Tan, Yap Peng
Keywords: Spectral clustering
Set-based face recognition
Issue Date: 2014
Source: Huang, L., Lu, J., & Tan, Y.-P. (2014). Co-Learned Multi-View Spectral Clustering for Face Recognition Based on Image Sets. IEEE Signal Processing Letters, 21(7), 875-879.
Series/Report no.: IEEE Signal Processing Letters
Abstract: 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.
ISSN: 1070-9908
DOI: 10.1109/LSP.2014.2319817
Schools: School of Electrical and Electronic Engineering 
Rights: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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