Abstract
We present a novel feature extraction method for face recog- nition called neighborhood discriminant embedding NDE, which incor- porates graph embedding and Fisher's criterion and includes an indi- vidual 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 tech- nique for discriminative feature extraction. IDF is proposed as an indi- vidual property of each sample to describe the contribution to classifica- tion. 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 da- tabases to demonstrate the effectiveness of our methods. © 2010 Society of
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