Face recognition with adaptive local hyperplane algorithm
The paper introduces a novel adaptive local hyperplane (ALH) classifier and it shows its superior performance in the face recognition tasks. Four different feature extraction methods (2DPCA, (2D)2PCA, 2DLDA and (2D)2LDA) have been used in combination with five classifiers (K-nearest neighbor (KNN), support vector machine (SVM), nearest feature line (NFL), nearest neighbor line (NNL) and ALH). All the classifiers and feature extraction methods have been applied to the renown benchmarking face databases—the Cambridge ORL database and the Yale database and the ALH classifier with a LDA based extractor outperforms all the other methods on them. The ALH algorithm on these two databases is very promising but more study on larger databases need yet to be done to show all the advantages of the proposed algorithm.
KeywordsFace recognition Adaptive local hyperplane ALH
- 5.Vincent P, Bengio Y (2002) K-local hyperplane and convex distance nearest neighbor algorithms. In: Advances in neural information processing systems (NIPS), vol 14. MIT Press, Cambridge, pp 985–992Google Scholar
- 6.Li SZ (1998) Face recognition based on nearest linear combinations. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. Santa Barbara, pp 839–844Google Scholar