Facial Expression Region Segmentation Based Approach to Emotion Recognition Using 2D Gabor Filter and Multiclass Support Vector Machine

Facial expressions have been studied extensively for the analysis of human sentiment properly. A human emotion recognition system through recognizing human facial expression is proposed in this paper. After preprocessing, segmentation of the facial expression regions is done in a unique yet effective and easy way to segment the left eye, right eye, nose, mouth properly from the facial region. 2D Gabor filter is used for the extraction of features from the expression regions. For reducing the dimension of the extracted features, downsampling and Principal Component Analysis (PCA) is used. For carrying out the classification task multiclass Support Vector Machine (SVM) is used for its ability to handle complex problems in high dimensional spaces. Three publicly available facial expression dataset was used to evaluate the performance of the proposed system. Finally, performance on these datasets by the proposed method is compared to previously attained performance by different methods which indicate that the proposed method attains state-of-the-art performance.