Non-linear fusion of local matching scores for face verification

This paper presents a face verification framework for fusing matching scores that measure similarities of local facial features. The framework is aimed to handle an open set verification scenario when users who try to enroll can be unknown to the system at the training phase. The kernel discriminant analysis is adopted within the framework to explore the discriminatory information of local matching scores in a high-dimensional non-linear space. A large sample size problem is raised for system training and an effective strategy is provided for tackling this problem. We demonstrate the framework by fusing the scores calculated using local binary pattern features. The experimental results show that our method improves the verification performance significantly when compared to a number of competitive techniques.