Nearest Neighbor Convex Hull Classification Method for Face Recognition

  • Xiaofei Zhou
  • Yong Shi
Conference paper

DOI: 10.1007/978-3-642-01973-9_64

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5545)
Cite this paper as:
Zhou X., Shi Y. (2009) Nearest Neighbor Convex Hull Classification Method for Face Recognition. In: Allen G., Nabrzyski J., Seidel E., van Albada G.D., Dongarra J., Sloot P.M.A. (eds) Computational Science – ICCS 2009. ICCS 2009. Lecture Notes in Computer Science, vol 5545. Springer, Berlin, Heidelberg

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 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiaofei Zhou
    • 1
  • Yong Shi
    • 1
    • 2
  1. 1.Research Center on Fictitious Economy and Data ScienceChinese Academy of SciencesBeijingChina
  2. 2.College of Information Science and TechnologyUniversity of Nebraska at OmahaOmahaUSA

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