Open-set face recognition by transductive kernel associative memory

Though a variety of face recognition techniques have been proposed in the literature, only a few of them considered open set recognition problems, which involves the rejection of unregistered subjects in addition to identifying persons registered in the database. Transductive confidence machine (TCM) is a novel strategy for classification associated with valid confidence, with recognition reliability as the ground for rejection. Many popular classification algorithms, such as k-nearest neighbor (kNN), can be plugged into the TCM framework and applied to open-set face recognition. As kernel associative memory model (KAM) has been proposed earlier as an efficient tool for close-set face recognition, this paper extends the KAM model into TCM by proposing a novel nonconformity measurement and corresponding TCM-kAM algorithm. Performance comparisons with published TCM-KNN open-set face recognition methods were conducted with ORL and AR faces, with verified advantages.