Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition

Ramin Irani, Kamal Nasrollahi, Marc Oliu Simon, Ciprian A. Corneanu, Sergio Escalera, Chris Bahnsen, Dennis H. Lundtoft, Thomas B. Moeslund, Tanja L. Pedersen, Maria-Louise Klitgaard, Laura Petrini

Abstract: Pain is a vital sign of human health and its automatic detection can be of crucial importance in many different contexts, including medical scenarios. While most available computer vision techniques are based on RGB, in this paper, we investigate the effect of combining RGB, depth, and thermal facial images for pain intensity level recognition. For this purpose, we extract energies released by facial pixels using a spatiotemporal filter. Experiments on a group of 12 elderly people applying the multimodal approach show that the proposed method successfully detects pain and recognizes between three intensity levels in 82% of the analyzed frames, improving by more than 6% the results that only consider RGB data.