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Exponential Neighborhood Preserving Embedding for Face Recognition
Ruisheng RAN Bin FANG Xuegang WU
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/05/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Pattern Recognition
neighborhood preserving embedding, matrix exponential, face recognition, the small-sample-size problem, manifold learning,
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Neighborhood preserving embedding is a widely used manifold reduced dimensionality technique. But NPE has to encounter two problems. One problem is that it suffers from the small-sample-size (SSS) problem. Another is that the performance of NPE is seriously sensitive to the neighborhood size k. To overcome the two problems, an exponential neighborhood preserving embedding (ENPE) is proposed in this paper. The main idea of ENPE is that the matrix exponential is introduced to NPE, then the SSS problem is avoided and low sensitivity to the neighborhood size k is gotten. The experiments are conducted on ORL, Georgia Tech and AR face database. The results show that, ENPE shows advantageous performance over other unsupervised methods, such as PCA, LPP, ELPP and NPE. Another is that ENPE is much less sensitive to the neighborhood parameter k contrasted with the unsupervised manifold learning methods LPP, ELPP and NPE.