Optimizing Facial Expression Recognition with Biogeography-Based Feature Selection

Facial expression recognition is a challenging research field in computer vision due to various issues such as occlusion, lighting conditions, camera pose angles, and the selection of relevant features. Extracting and selecting pertinent features from facial images is crucial in achieving efficient expression recognition. This paper proposes a metaheuristic-based feature selection and classification methodology using the Biogeography-Based Optimization (BBO) algorithm to select the best-performing features and optimize the recognition accuracy of the classifier. The cross-validation recognition accuracy of the Support Vector Machine (SVM) is used as the evaluation criterion in the BBO algorithm to choose the optimal feature subset from the extracted features. The performance of the proposed BBO-SVM feature selection model is compared with other filter-based approaches. Experiments are conducted on three publicly available databases: JAFFE, MUG, and CK+, to validate the performance of the proposed system. The model achieves promising recognition accuracy across all datasets, with results compared to similar works presented in the literature.