Face Expression Recognition with the Optimization based Multi-SVNN Classifier and the Modified LDP Features

摘要

Facial expression recognition (FER) is the interesting research area that enables us to recognize the expression of the human face in the day-to-day life. Most of the traditional methods fail to recognize the expressions accurately as the expressions are based on the movements of the parts in the human face. The paper proposes the effective method of FER using the proposed Whale- Grasshopper Optimization algorithm based Multi-Support Vector Neural Network (W-GOA-based MultiSVNN). The features from the facial image is extracted using the Scale-Invariant Feature Transform (SIFT) and the proposed Scatter Local Directional Pattern (SLDP). The extracted features are classified using the proposed classifier to recognize the expression of the face. The proposed method of facial recognition enhances the recognition accuracy. The experimentation of the proposed algorithm is performed using the databases, such as Cohn-Kanade AU-Coded Expression Database and The Japanese Female Facial Expression (JAFFE) Database. The proposed algorithm outperforms the existing methods in terms of the accuracy, TPR, and FPR and the values are found to be 0.96, 0.96, and 0.009, respectively. (C) 2019 Elsevier Inc. All rights reserved.