Facial expression recognition via sparse representation using positive and reverse templates

Abstract

This study models facial expression recognition with a sparse representation classification (SRC) method. By analysing SRC's robustness to noise, this study further proposes an SRC method based on positive and reverse templates (PRTs-SRC), which uses PRTs to expand an over-complete dictionary constructed by training samples. The expanded dictionary can contain more information, and increase the robustness to noise. To validate the performance of the proposed algorithm, experiments were carried out on relevant expression databases. The authors compared and analysed the recognition performances for the proposed algorithm and other methods. The results show that even with high noise levels, the proposed algorithm performs above 80% recognition rate.