Synergistic Generic Learning for Face Recognition From a Contaminated Single Sample per Person

Single sample per person face recognition (SSPP FR), i.e., identifying a person (i.e., data subject) with a single face image only for training, has several attractive potential applications, but it is still a challenging problem. Existing generic learning methods usually leverage prototype plus variation (P+V) model for SSPP FR provided that face samples in the biometric enrolment database are variation-free and thus can be treated as the prototypes of data subjects. However, this condition is not satisfied when these samples are contaminated by nuisance facial variations in the wild, such as varied expressions, poor lightings, and disguises (e.g., wearing scarf). We call this new and practical problem SSPP FR with a c ontaminated biometric e nrolment database (SSPP-ce FR). Subsequently, a challenging issue will be raised on estimating proper prototypes from the contaminated enrolment samples in SSPP-ce FR. Moreover, the generated variation dictionary also needs to be enhanced because it is simply based on the subtraction of average face from the samples of the same data subject in the generic set, thus containing individual characteristics that can hardly be shared by other data subjects. To address these two issues, we propose a novel synergistic generic learning (SGL) method to study the SSPP-ce FR problem. Compared with the existing generic learning methods, SGL develops a new 'learned P + learned V' model to identify new query samples. Specifically, it learns better prototypes for the contaminated samples in the biometric enrolment database by preserving their more discriminative subject-specific portions and learns a representative variation dictionary by extracting the less discriminative intra-subject variants from an auxiliary generic set. The experiments on various benchmark face datasets demonstrate the effectiveness of the proposed SGL method.