Combining local similarity measures: summing, voting, and weighted voting

Recent research on human face recognition has shown that local features have advantages over global features because local features are more robust to some changes of facial expression, as well as shift, rotation and tilt. In this paper, we experimentally investigate the commonly used summing strategy as well as the voting method in combining the local distances/similarity into the final decision. We proposed and analyzed a new classification method based on weighted voting that allows for each local window to cast not just a single vote, but a set of weighted votes. Experimental results are given on two large face databases: the CNNL and FERET databases. The results show that the weighted voting strategy outperforms simple voting, and the commonly used method of summing local distances.