Facial expression recognition using log-Gabor filters and local binary pattern operators

Abstract

This study investigates two different methods of feature extraction for person-independent facial expression recognition from images. The logarithmic Gabor filters and the local binary pattern operator (LBP) were used for feature extraction. Then, the optimum features were selected based on minimum redundancy and maximum relevance algorithm (MRMR). Six different facial expressions were considered. The selected features were classified using the naive bayesian (NB) classifier. The percentage of correct classifications varied across different expressions from 62.8% to 90.5% for the log-Gabor filter approach,and from 71.8% to 94.0% for the LBP approach. Experiments carried out on Cohn-Kanade database showed comparable performance between Log-Gabor filters and LBP operator, with a classification accuracy of around 82.3% and 81.7% respectively. This was achieved on low-resolution images, without the need to precisely locate facial points on each face image