Chapter

Computational Science – ICCS 2009

Volume 5545 of the series Lecture Notes in Computer Science pp 570-577

Nearest Neighbor Convex Hull Classification Method for Face Recognition

  • Xiaofei ZhouAffiliated withResearch Center on Fictitious Economy and Data Science, Chinese Academy of Sciences
  • , Yong ShiAffiliated withResearch Center on Fictitious Economy and Data Science, Chinese Academy of SciencesCollege of Information Science and Technology, University of Nebraska at Omaha

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Abstract

In this paper, nearest neighbor convex hull (NNCH) classification approach is used for face recognition. In NNCH classifier, a convex hull of training samples of a class is taken as the distribution estimation of the class, and Euclidean distance from a test sample to the convex hull (the distance is called convex hull distance) is taken as the similarity measure for classification. Experiments on face data show that the nearest neighbor convex hull approach can lead to better results than those of 1-nearest neighbor (1-NN) classifier and SVM classifiers.

Keywords

classification SVM convex nearest neighbor convex hull face recognition