NDLI: Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Content Provider	IEEE Xplore Digital Library
Author	Wang-Meng Zuo Kuan-Quan Wang David Zhang
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., China (Wang-Meng Zuo; Kuan-Quan Wang)
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.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4870
Ending Page	4875
File Size	461214
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Assembly Face recognition Principal component analysis Image recognition Image databases Image representation Feature extraction Covariance matrix Optimized production technology Spatial databases Palmprint Recognition 2DPCA Assemble Matrix Metric Image Recognition Face Recognition
Content Type	Text
Resource Type	Article

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