Evolutionary fusion of local texture patterns for facial expression recognition

This paper presents a simple, yet effective facial feature descriptor based on evolutionary synthesis of different local texture patterns. Unlike the traditional face descriptors that exploit visually-meaningful facial features, the proposed method adopts a genetic programming-based feature fusion approach that utilizes different local texture patterns and a set of linear and nonlinear operators in order to synthesize new features. The strength of this approach lies in fusing the advantages of different state-of-the-art local texture descriptors and thus, obtaining more robust composite features. Recognition performance of the proposed method is evaluated using the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, facial features synthesized based on the proposed approach yield an improved recognition performance, as compared to some well-known face feature descriptors.