Feature extraction for nearest neighbor classification: Application to gender recognition

In this article, we perform an extended analysis of different face-processing techniques for gender recognition problems. Prior research works show that support vector machines (SVM) achieve the best classification results. We will show that a nearest neighbor classification approach can reach a similar performance or improve the SVM results, given an adequate selection of features of the input data. This selection is performed using a dimensionality reduction technique based on a modification of nonparametric discriminant analysis, designed to improve the nearest neighbor classification. The choice of nearest neighbor is especially justified by the use of a large database. We also analyze a nonlinear algorithm, locally linear embedding, and its supervised version. Given that this technique is focused on preserving the local configuration of the neighborhood of each point, it should be a priori a good dimensionality reduction technique for extracting good features for nearest neighbor classification. A complete comparative study with the most classical face-processing techniques is also performed. © 2005 Wiley Periodicals, Inc.