Personalized face verification system using owner-specific cluster-dependent LDA-subspace

We propose an owner-specific cluster-dependent linear discriminant analysis (OSCD-LDA) method, and apply it to develop a personalized face verification system. Before the owner enrollment, our system first divides all the training face images into a number of clusters, each containing a subset of face images having similar characteristics. Once the owner completes the enrollment procedure, the system assigns the owner to the cluster that contains faces most similar to the owner´s training faces. Then, the system uses the training faces in this most similar cluster to determine the OSCD-LDA subspace for computing the matching score. This OSCD-LDA subspace can be considered as a personalized subspace, trained specifically for this owner in order to best discriminate this particular owner from other non-owners. Our experimental results have shown that the proposed OSCD-LDA method outperforms the conventional LDA method, and can reduce false acceptance rate and false rejection rate by about 40 percent when using the XM2VTS database.