Posed and spontaneous facial expression differentiation using deep Boltzmann machines

Current works on differentiating between posed and spontaneous facial expressions usually use features that are handcrafted for expression category recognition. Till now, no features have been specifically designed for differentiating between posed and spontaneous facial expressions. Recently, deep learning models have been proven to be efficient for many challenging computer vision tasks, and therefore in this paper we propose using the deep Boltzmann machine to learn representations of facial images and to differentiate between posed and spontaneous facial expressions. First, faces are located from images. Then, a two-layer deep Boltzmann machine is trained to distinguish posed and spon-tanous expressions. Experimental results on two benchmark datasets, i.e. the SPOS and USTC-NVIE datasets, demonstrate that the deep Boltzmann machine performs well on posed and spontaneous expression differentiation tasks. Comparison results on both datasets show that our method has an advantage over the other methods.