Incorporating Generic Learning to Design Discriminative Classifier Adaptable for Unknown Subject in Face Verification

Abstract

In recent years, there has been a growing interest on the verification of unspecific person, which requires the system adaptable for unknown new subject. Most of previous works used generative methods. In this paper, we propose a discriminative method, Bayesian Competitive Model, to explicitly handle the person-unspecific problem. The key idea originates from the observation that it is possible to design a discriminative classifier adaptable for unknown new subject when generic learning is applied. The generic learning functions in two aspects: First, it learns the generic distribution of faces, and thus provides a MAP framework for verification. Second, it learns the intra-personal variations of numerous known persons to infer the distribution of the unknown new subject. Both distributions are formulated in GMM model, respectively. To further improve the performance, we integrate Bayesian Competitive Model with a generative classifier based on confidence. A number of experiments on the BANCA dataset demonstrate the effectiveness of the new algorithm in handling the personunspecific problem, and its advantage over existing algorithms.