Spontaneous versus posed smile recognition using discriminative local spatial-temporal descriptors

Automatic recognition of spontaneous versus posed (SVP) facial expressions has received widespread attention in recent years for its potential applications in friendly human machine interface. Most existing works of SVP facial expression recognition extract geometry-based features which heavily rely on accurate detection and tracking of facial feature points. In this paper, a novel approach is proposed to distinguish between spontaneous and posed smiles using discriminative completed LBP from three orthogonal planes, which is an appearance-based local spatial-temporal descriptor. The descriptor devotes to extracting most robust and discriminative patterns of interest. In addition, flexible facial subregion cropping, a spatial division method, is proposed taking into account different facial organ size of different people and filtering of redundant information. Besides, in the temporal domain, a new division method is also applied, which divides the smile process according to smile dynamics. Experiments on three benchmark databases and comparisons to the state-of-the-art methods validate the advantages of our approach, obtaining an accuracy rate of 91.40%.