Subject-dependent selection of geometrical features for spontaneous emotion recognition

Facial expressions are among the most powerful ways to reveal the emotional state. Therefore, Facial Expression Recognition (FER) has been widely introduced to wide fields of applications, such as security, psychotherapy, neuromarketing, and advertisement. Feature extraction and selection are two essential key issues for the design of efficient FER systems. However, most of the previous studies focused on implementing static feature selection methods. Although these methods have shown promising results, they still present weaknesses, especially when dealing with spontaneous expressions. This is mainly due to the specificity of each face, which makes the facial emotion display differs from one subject to another. To address this problem, we propose a face-based dynamic feature selection of two geometric features sub-classes, namely linear and eccentricity features. This combination provides a better understanding of the facial transformation during the emotion display. Moreover, the suggested selection method takes into consideration the subject’s general facial structure, muscle movements, and head position. The performed experiments, using the CK+ and the DISFA datasets, have showed that the proposed method outperforms the state-of-the-art techniques and maintains superior performance with cross-dataset validation. In fact, the accuracy of facial expression recognition by the proposed method reaches 97.72% and 91,26% on the CK+ and the DISFA datasets, respectively.