Facial expression recognition from infrared thermal images using temperature difference by voting

This paper proposes an approach of facial expression recognition from infrared thermal images by using temperature difference features and voting strategy. Firstly, three kinds of temperature features named horizontal, vertical and sequential difference grid features are introduced and extracted from the thermal images of four facial regions. Secondly, K-Nearest Neighbor is used as a classifier in each facial region. After that, a voting strategy is used as the decision-level fusion. Experiments on a large scale infrared thermal expression database achieve around 61.62% recognition rate. The comparative experiment results suggest that face-region-based facial expression classification using the temperature difference features is feasible, and demonstrate that difference grid features are independent and insensitive to individual or environment. The voting results provide the evidence that our face-region-based voting strategy using infrared thermal images for facial expression recognition is reliable and effective.