Analysis of range images used in 3D facial expression recognition

Researches on 3D facial expression recognition (3D FER) have been greatly fostered by the creation of BU-3DFE (Binghamton University 3D Facial Expression) database, but given limited 3D methods, they encounter some bottlenecks now. A new approach to solve this problem is to transform facial expression from 3D models into 2D data, thus range image emerges, playing an intermediary role in current researches. There are various range images up to date, but it´s relatively tough to identify which one is optimal for 3D FER. This paper aims to analyse the performances of different range images which are generated for 3D FER. Therefore, different features and classifiers are investigated in expression recognition framework. The recognition ratios of proposed experiments are higher than 88%, almost falling within the same scope. It reveals that range images still retain main discriminative information, which is significant for 3D FER. Also, there are a few differences among various range images´ performances. More importantly, the range images can make 3D FER implement automatically. The findings above are so remarkable that they would benefit the future research of 3D facial expression recognition.