Face recognition via adaptive discriminant clustering

This paper presents a methodology that tackles the face recognition problem by accommodating multiple clustering steps. At each clustering step, the test and training faces are projected to a discriminant space and the projected training data are partitioned into clusters using the k-means algorithm. Then a subset of the training data clusters is selected, based on how similar the faces in these clusters are to the test face. In the clustering step that follows a new discriminant space is defined by processing this subset and both the test and training data are projected to this space. This process is repeated until one final cluster is selected and the most similar, to the test face, face class contained is set as the identity match. The UMIST and XM2VTS face databases have been used to evaluate the algorithm and results indicate that the proposed framework provides a promising solution to the face recognition problem.