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Optical Engineering

Neighborhood discriminant embedding in face recognition
Author(s): Dexing Zhong; Jiuqiang Han; Xinman Zhang; Yongli Liu
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Paper Abstract

We present a novel feature extraction method for face recognition called neighborhood discriminant embedding (NDE), which incorporates graph embedding and Fisher's criterion and includes an individual discriminative factor (IDF). Graph embedding is able to reveal the representative and discriminative features from the underlying nonlinear face data structure. Fisher's criterion is recognized as an effective technique for discriminative feature extraction. IDF is proposed as an individual property of each sample to describe the contribution to classification. NDE can remain the local structure of the nearest neighbors of each data point during the dimensionality reduction as well as gather the within-class points and separate the between-class points in the low-dimensional projected space. Utilizing Fisher's criterion and taking into account IDF, the discriminative capability of NDE is further enhanced. Comprehensive experiments are conducted using the Olivetti Research Laboratory (ORL) and Facial Recognition Technology (FERET) face databases to demonstrate the effectiveness of our methods.

Paper Details

Date Published: 1 July 2010
PDF: 7 pages
Opt. Eng. 49(7) 077203 doi: 10.1117/1.3465582
Published in: Optical Engineering Volume 49, Issue 7
Show Author Affiliations
Dexing Zhong, Xi'an Jiaotong Univ. (China)
Jiuqiang Han, Xi'an Jiaotong Univ. (China)
Xinman Zhang, Xi'an Jiaotong Univ. (China)
Yongli Liu, Xi'an Jiaotong Univ. (China)


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