Dynamic training using multistage clustering for face recognition

A novel face recognition algorithm that uses dynamic training in a multistage clustering scheme is presented and evaluated. This algorithm uses discriminant analysis to project the face classes and a clustering algorithm to partition the projected face data, thus forming a set of discriminant clusters. Then, an iterative process creates subsets, whose cardinality is defined by an entropy-based measure, that contain the most useful clusters. The best match to the test face is found when only a single face class is retained. This method was tested on the ORL, XM2VTS and FERET face databases, whereas the UMIST database was used in order to train the proposed algorithm. Experimental results indicate that the proposed framework provides a promising solution to the face recognition problem.