Combining Gabor features: summing vs. voting in human face recognition

Gabor wavelet-based feature extraction has been emerging as one of the most promising ways to represent human face image data. In this paper, we examine the performance of two types of classifiers that can be used with Gabor features. In the first classifier, the distance between two images is computed by summing the local distances among all the nodes. In the second classifier, a voting strategy is used In addition, we examine two types of shift optimization procedures. The first is the standard elastic graph matching algorithm, and the second is a constrained version of the algorithm. Experimental results indicate that the voting-based classifier with constrained elastic graph matching gives improved results.