Discriminant-Reconstruction Based Multiple Attribute Classifiers for Face Recognition

Abstract

In the past few years, face attributes have attracted much attention. In this paper, for the first time we combine the discriminant subspace learning technique with the idea of pattern reconstruction to build a face attribute classification framework. For the attribute considered, the framework firstly learns an attribute subspace by using a discriminant subspace learning method, which also has the capability of pattern reconstruction. The framework then reconstructs the attribute state of input query image with the learned subspace, and classifies face attribute based on minimum reconstruction error. By repeatedly using the classification framework for different attributes, we can achieve multiple classification results output. According to the output, we select matching objects for each given query image based on generalized hamming distance to realize face recognition. The proposed attribute classification framework and face recognition approach are validated on the public AR and Weizmann face databases. Experimental results demonstrate their effectiveness as compared with several related methods.