Technical Program

Paper Detail

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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