Convolutional Neural Network-BO Based Feature Extraction and Multi-Layer Neural Network-SR Based Classification for Facial Expression Recognition

Facial expression recognition has been more essential in artificial machine intelligence systems in recent years. Recognizing facial expressions automatically has constantly been considered as a challenging task since people significantly vary the way of exhibiting their facial expressions. Numerous researchers established diverse approaches to analyze the facial expressions automatically but there arise few imprecision issues during facial recognition. To address such shortcomings, our proposed approach recognizes the facial expressions of humans in an effective manner. The suggested method is divided into three stages: pre-processing, feature extraction, and classification. The inputs are pre-processed at the initial stage and CNN-BO algorithm is used to extract the best feature in the feature extraction step. Then the extracted feature is provided to the classification stage where MNN-SR algorithm is employed in classifying the face expression as joyful, miserable, normal, annoyance, astonished and frightened. Also, the parameters are tuned effectively to obtain high recognition accuracy. In addition to this, the performances of the proposed approach are computed by employing three various datasets namely; CMU/VASC, Caltech faces 1999, JAFFE and XM2VTS. The performance of the proposed system is calculated and comparative analysis is made with few other existing approaches and its concluded that the proposed method provides superior performance with optimal recognition rate.