Face recognition and verification with pose and illumination variations and imposter rejection

Abstract

We address rejection-classification problems, which have been ignored in most prior work. For such a system, a high classification rate and a low false alarm rate are simultaneously desired. We first propose a one-class support vector representation machine (SVRM). The SVRM achieves a high test set detection rate by requiring a high training set detection rate; the SVRM reduces the false alarm rate by minimizing the upper bound of the decision region. The SVRM is then extended to a new support vector representation and discrimination machine (SVRDM) classifier to address multiple-class cases. The theoretical basis for our new SVRDM as best at rejection of non-objects (imposters in face recognition) is provided, as are new σ parameter selection methods. Test results on face recognition and verification with both pose and illumination variations are presented.