NDLI: Two-Dimensional Technique for Image Presentation and its Application

Content Provider	IEEE Xplore Digital Library
Author	Yong Xu Jing-Yu Yang Zhen-Min Tang Chun-Xia Zhao
Copyright Year	2006
Description	Author affiliation: Bio-Comput. Res. Center, Harbin Inst. of Tech., Shenzhen (Yong Xu; Jing-Yu Yang; Zhen-Min Tang; Chun-Xia Zhao)
Abstract	In contrast with PCA, the two-dimension presentation technique (TDP) developed recently is very efficient. With TDP, we can easily extract feature vectors of an image matrix by projecting the image matrix rather than the corresponding vector onto projecting axes. In this paper, we present complete properties of TDP in detail and the property of decorrelation associated with TDP is originally revealed. The differences and similarities between TDP and PCA are also analyzed and presented. Furthermore, local-TDP approach is proposed to perform face recognition. Local-TDP aims to draw local characteristic of face images. Especially, local-TDP appears to be beneficial to weaken the side effect on face recognition of varying imaging conditions. The possible reason is that the varying imaging conditions mainly bring strong difference for parts of the image, while the influence on other parts is little. As a result, the similarity between the extracted local features of two face images of one individual may become larger in comparison with holistic features of face images. The conducted experiment also indicates that local-TDP is competent for extracting invariant features of face images with varying illumination
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	4376
Ending Page	4382
File Size	256360
Page Count	7
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.259088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Covariance matrix Feature extraction Face recognition Application software Face detection Computer science Decorrelation Lighting Image reconstruction reconstruction error PCA correlation coefficient uncorrelated features
Content Type	Text
Resource Type	Article

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