Pose-Invariant Facial Expression Recognition Based on MOEO Algorithm and LBP

This paper focuses on developing an innovative method that incorporates facial features, such as mouth, and eyebrows to identify emotions from an image. The proposed system can also recognize emotions from images with the facial pose variation and occlusions. Our findings indicate that this new system effectively identifies critical and comprehensive features. These features are then processed through a second phase involving a multi-objective optimization technique. This technique accurately predicts emotions in an image, showing superior performance compared to many standard and deep learning-based methods. Our approach is particularly adept at changes in facial angles, outperforming many conventional and advanced models. Our model's better performance in emotion recognition is due to its ability to choose the optimal solution from a range of possible solutions, which allows it to accurately represent the most suitable emotional expression seen in the face images.