One sample per person face recognition via sparse representation

One sample per person face recognition (OSPP) is a challenging problem in face recognition community. Lack of samples leads to performance deterioration. Extended sparse representation-based classifier (ESRC) demonstrates excellent performance on OSPP. However, because there are intra-class variant atoms in the dictionary of ESRC, the number of atoms in the dictionary is always large and it will spend a long time during recognition. In this study, the authors propose a new OSPP face recognition algorithm via sparse representation (OSPP-SR). A compressed dictionary and a new identification strategy are provided in OSPP-SR. It is proved theoretically and experimentally that OSPP-SR reaches better or similar performance but spends less time than ESRC. Experiments are conducted on three different databases (extended Yale Face database B, AR database and FERET database) to show the validity of OSPP-SR. Images under clean and noise conditions are also tested to evaluate the robustness of OSPP-SR.