A Weighted Voting and Sequential Combination of Classifiers Scheme for Human Face Recognition

In this paper, we examine the performance of a weighted voting classification strategy for human face recognition. Here, local template matching is used, but instead of summing the local distance measures, a weighted voting scheme based on rank information is used to combine the results of the local classifiers. This strategy can be used with any suitable features; for example, simple pixel features, or Gabor features, etc. If multiple features are available, we show how a sequential combination strategy can be devised to efficiently and reliably compute the final classifier output. Test results are presented for the problem of human face recognition on a large database of faces.