A genetic programming-based feature selection and fusion for facial expression recognition

Emotion recognition has become one of the most active research areas in pattern recognition due to the emergence of human-machine interaction systems. Describing facial expression is a very challenging problem since it relies on the quality of the face representation. A multitude of features have been proposed in the literature to describe facial expression. None of these features is universal for accurately capturing all the emotions since facial expressions vary according to the person, gender and type of emotion (posed or spontaneous). Therefore, some research works have considered combining several features to enhance the recognition rate. But they faced significant problems because of information redundancy and high dimensionality of the resulting features. In this work, we propose a genetic programming framework for feature selection and fusion for facial expression recognition, which we called GP - FER. The main component of this framework is a tree-based genetic program with a three functional layers (feature selection, feature fusion and classification). The proposed genetic program is a binary classifier that performs discriminative feature selection and fusion differently for each pair of expression classes. The final emotion is captured by performing a unique tournament elimination between all the classes using the binary programs. Three different geometric and texture features were fused using the proposed GP - FER. The obtained results, on four posed and spontaneous facial expression datasets (DISFA, DISFA+, CK+ and MUG), show that the proposed facial expression recognition method has outperformed, or achieved a comparable performance to the state-of-the-art methods. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Emotion recognition is a challenging problem in pattern recognition field, as it relies on the quality of face representation and lacks universal features to accurately capture all emotions. Combining multiple features to enhance recognition rate faces issues such as information redundancy and high dimensionality. A genetic programming framework called GP-FER is proposed in this work to address these challenges and has demonstrated superior performance on various facial expression datasets.