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. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Zhou, X., & Shi, Y. (2009). Nearest neighbor convex hull classification method for face recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5545 LNCS, pp. 570–577). https://doi.org/10.1007/978-3-642-01973-9_64
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