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Optical Engineering

Feature space-based human face image representation and recognition
Author(s): Yong Xu; Zizhu Fan; Qi Zhu
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Paper Abstract

We propose a novel face recognition method that represents and classifies face images in the feature space. It first assumes that in the feature space the test sample can be well expressed by a linear combination of the training samples, and then it exploits the obtained linear combination to perform face recognition. We also present the foundation, rationale, and characteristics of, as well as the differences between, our method and conventional kernel methods. The analysis shows that our method is a representation-based kernel method and works in the feature space. This method might be able to outperform the representation-based methods that work in the original space. The experimental results show that our method partially possesses the properties of "sparseness" and is able to reduce greatly the effects of noise and occlusion in the test sample.

Paper Details

Date Published: 7 February 2012
PDF: 8 pages
Opt. Eng. 51(1) 017205 doi: 10.1117/1.OE.51.1.017205
Published in: Optical Engineering Volume 51, Issue 1
Show Author Affiliations
Yong Xu, Harbin Institute of Technology (China)
Zizhu Fan, Harbin Institute of Technology (China)
Qi Zhu, Harbin Institute of Technology (China)

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