Spatio-temporal Analysis for Infrared Facial Expression Recognition from Videos

Facial expression recognition (FER) for emotion inference has become one of the most important research fields in human-computer interaction. Existing study on FER mainly focuses on visible images, whereas varying lighting conditions may influence their performances. Recent studies have demonstrated the advantages of infrared thermal images reflecting the temperature distributions, which are robust to lighting changes. In this paper, a novel infrared image sequence based FER method is proposed using spatiotemporal feature analysis and deep Boltzmann machines (DBM). Firstly, a dense motion field among infrared image sequences is generated using optical flow algorithm. Then, PCA is applied for dimension reduction and a three-layer DBM structure is designed for final expression classification. Finally, the effectiveness of the proposed method is well demonstrated based on several experiments conducted on NVIE database.