Texture Features for Facial Expression Recognition using Machine Learning Methods
This paper presents the efficient method to classify the facial expressions based on texture features using multiple machine learning methods. To carry out the proposed experiment the dataset contains 31398 face images belongs to seven classes are; Angry, Sad, Surprise, Disgust, Fear and Neutral are considered. In the process of features extraction the LBP and HOG methods were used. In the process of LBP feature extraction the upright technique is applied and from this technique the 10 potential features are extracted and from HOG method 81 features are extracted. The extracted features are submitted to KNN, SVM, LDA, and MLP classifier. From the MLP we obtained the 98% highest recognition accuracy.