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Title: Assembled matrix distance metric for 2DPCA-based face and palmprint recognition
Authors: Zuo, W
Wang, K
Zhang, DD 
Issue Date: 2005
Source: Proceedings of the 2005 International Conference on Machine Learning and Cybernetics : August 18-21, 2005, Guangzhou, China, p. 4870-4875
Abstract: Two-dimensional Principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One advantage of 2DPCA is that it can extract feature matrix using a straightforward image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two image databases, the ORL face and the PolyU palmprint. The experimental results show that the assembled matrix distance metric is very effective in 2DPCA based image recognition.
Keywords: 2DPCA
Assemble matrix metric
Image recognition
Face recognition
Palmprint recognition
Publisher: IEEE
ISBN: 0-7803-9091-1
Rights: © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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