Facial expression recognition using intra‐class variation reduced features and manifold regularisation dictionary pair learning

Abstract

A novel framework, named intra‐class variation reduced features‐based manifold regularisation dictionary pair learning model, is presented for solving facial expression recognition (FER) tasks. Since a query face and its corresponding image with intra‐class variations (e.g. identity and illumination) are similar in appearance, the authors generate intra‐class variation reduced features (IVRF) from the difference between a query face image and its corresponding estimated image of each expression class. IVRF can reduce negative influence from the intra‐class variations and make their model robust to intra‐class variations. Furthermore, a manifold regularisation term is incorporated into the dictionary pair learning model, which leads to a smoothly varying sparse representation. Their model fully takes advantage of the geometrical structure of data, which benefits the FER task. The experimental results on two public databases verify the effectiveness and superiority of their method and indicate its promising capability in expression discrimination.

Keywords

• facial expression recognition
• intraclass variation
• manifold regularisation dictionary pair learning model
• reduced feature dictionary pair learning
• FER
• intraclass variation reduced feature generation