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Title: Bi-directional PCA with assembled matrix distance metric
Authors: Zuo, W
Wang, K
Zhang, DD 
Issue Date: 2005
Source: 2005 ICIP : 2005 International Conference on Image Processing (ICIP) : September 11-14, 2005, Genova, Italy, v. 2, p. 958-961
Abstract: Principal Component Analysis (PCA) has been very successful in image recognition. Recent researches on PCA-based methods are mainly concentrated on two issues, feature extraction and classification. In this paper we propose Bi-Directional PCA (BDPCA) with assembled matrix distance (AMD) metric to simultaneously deal with these two issues. For feature extraction, we propose a BDPCA approach which can reduce the dimension of the original image matrix in both column and row directions. For classification, we present an AMD metric to calculate the distance between two feature matrices. The results of our experiments show that, BDPCA with AMD metric is very effective in image recognition.
Keywords: PCA
Image recognition
Face recognition
Palmprint recognition
Publisher: IEEE
ISBN: 0-7803-9134-9
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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