Analysis of WD face dictionary for sparse coding based face recognition

Abstract

This paper deals with the analysis of WD Face dictionary for sparse coding based face recognition. WD (weighted decomposition) Face dictionary emphasizes subject specific unique information of a person. This dictionary has an advantage to adapt to the nature of training images. In the resultant dictionary rows are uncorrelated, which is an essential criterion for dictionary to ensure sparse representation of coefficient vector. The range of sparsity determined by calculating the lower and upper bounds of sparse recovery of coefficient vector for WD Face dictionary exhibits its capability to sparsely represent a test image as a linear combination of training images, even when available training images are small in number. Experimental results solidify our proposal that sparse coding based face recognition with WD Face dictionary is preferable to the existing face recognition techniques. © 2013 Springer-Verlag.