A comparison study of feature spaces and classification methods for facial expression recognition

Facial expression recognition (FER) is important for robots and computers to achieve natural interaction with human. Over the years, researchers have proposed different feature descriptors, implemented different classification methods, and carried out test experiments on different datasets in realizing an automatic FER system. While achieving good performance, the most efficient feature space and classification method for FER remain unknown due to lack of comparison study. We performed comprehensive comparison experiments with five popular feature spaces in computer vision field and seven classification methods with four unique facial expression datasets. Our contributions in this work includes: (1) identified most efficient feature space for FER, (2) investigated effect of image resolutions on FER performances, and (3) obtained best FER performance by using AdaBoost algorithm for feature selection and Support Vector Machine for image classification.