Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine

Abstract

This paper presents the used of histogram of oriented gradient (HOG) for facial expression recognition using support vector machine (SVM). In this work, the facial expression images are firstly preprocessed by face detection and cropped images. Then, HOG method is adopted as feature extraction on facial image. The ability of HOG to preserve the local information and orientation density distribution in facial images suitable as shape descriptor for facial expression. It divides the image into cell or patch that has magnitude and orientations. The extracted HOG was then concatenated into histogram bin to form one feature vector before feed into SVM classifier. Both JAFFE and KDEF datasets were employed to evaluate the performance of proposed method. Based on results, the average recognition rates of JAFFE and KDEF datasets are 76.19% and 80.95% respectively. The results show that the performance of expression surprise has outperformed compared to others expression while expression fear contributes the lowest recognition rate. Thus, utilization of HOG features with SVM classifier have shown the promising results in recognizing facial expression.