Automated facial expression recognition based on histograms of oriented gradient feature vector differences. Issue 1 (December 2015) - Mlakar, Uroš
Abstract This article proposes an efficient automated method for facial expression recognition based on the histogram of oriented gradient (HOG) descriptor. This subject-independent method was designed for recognizing six prototyping emotions. It recognizes emotions by calculating differences on a level of feature descriptors between a neutral expression and a peak expression of an observed person. The parameters for the HOG descriptor were determined by using a genetic algorithm. Support vector machines (SVM) were applied during the recognition phase, whereat one SVM classifier was trained for one emotion. Each classifier was trained using difference vectors obtained by subtraction of HOG feature vectors calculated for the neutral and apex emotion subjects image. The proposed method was tested by using a leave-one-subject-out validation strategy for 106 subjects on 1232 images from the Cohn Kanade, and for 10 subjects on 192 images from the JAFFE database. A mean recognition rate of 95.64 % was obtained using the Cohn Kanade database, which is higher than the recognition rates for almost all other single-image- or video-based methods for facial emotion recognition.