Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15566
Title: Two-dimensional discriminant transform for face recognition
Authors: Yang, J
Zhang, D 
Yong, X
Yang, JY
Keywords: Face recognition
Feature extraction
Fisher linear discriminant analysis (fld or lda)
Fisherfaces
Two-dimensional data analysis
Issue Date: 2005
Publisher: Elsevier
Source: Pattern recognition, 2005, v. 38, no. 7, p. 1125-1129 How to cite?
Journal: Pattern recognition 
Abstract: This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.
URI: http://hdl.handle.net/10397/15566
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2004.11.019
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