| Paper: | MLSP-P5.3 | ||
| Session: | Image and Video Processing | ||
| Time: | Thursday, May 20, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | FACE RECOGNITION USING TWO NOVEL NEAREST NEIGHBOR CLASSIFIERS | ||
| Authors: | Wenming Zheng; Southeast University | ||
| Cairong Zou; Southeast University | |||
| Li Zhao; Southeast University | |||
| Abstract: | In this paper, two novel classifiers based on local nearest neighborhood rule, called nearest neighbor line (NNL) and nearest neighbor plane (NNP), are presented for face recognition. The underlying idea of both classifiers is the local linear combination technique that has been previously used in locally linear embedding (LLE) for nonlinear dimension reduction. Comparison to other linear combination based classifiers such as the nearest feature line (NFL) and the nearest feature plane (NFP), the proposed methods take much lower computation cost. Furthermore, the experimental results on the ORL face database have shown that the performance of both proposed methods are competitive to the NFL and NFP in face classification. | ||
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