A remarkable standard for estimating the performance of 3D facial expression features

All the previous work on 3D facial expression recognition is always based on different feature extraction algorithms and different classifiers, so there is no uniform standard for us to identify which features are the “best”. This paper investigates KL divergence (relative entropy) for discrimination power computation to determine the “best” features in this field. From experiments, we can conclude that local facial expression features in flow-matrix form are more beneficial to 3D facial expression recognition than in geometry-matrix form; the feature points in local expression regions can be more discriminative than points in face contour; and the slope and angle features are more powerful than distance features. Above all, this paper verifies that the KL divergence can definitely be considered as the standard for determining the “best” features to recognize 3D facial expressions. This is the first exploration on BU-3DFE (Binghamton University 3D Facial Expression) database to find a standard for evaluating the extracted facial expression features, and all of these results are remarkable for 3D facial expression feature extraction.