The Community for Technology Leaders
2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 2160-7516
ISBN: 978-1-4673-6758-5
pp: 88-95
Ramin Irani , Visual Analysis of People (VAP) Laboratory, Rendsburggade 14, 9000 Aalborg, Denmark
Kamal Nasrollahi , Visual Analysis of People (VAP) Laboratory, Rendsburggade 14, 9000 Aalborg, Denmark
Marc O. Simon , Computer Vision Center, UAB, Edificio O, Campus UAB, 08193, Bellaterra (Cerdanyola), Barcelona, Spain
Ciprian A. Corneanu , Computer Vision Center, UAB, Edificio O, Campus UAB, 08193, Bellaterra (Cerdanyola), Barcelona, Spain
Sergio Escalera , Computer Vision Center, UAB, Edificio O, Campus UAB, 08193, Bellaterra (Cerdanyola), Barcelona, Spain
Chris Bahnsen , Visual Analysis of People (VAP) Laboratory, Rendsburggade 14, 9000 Aalborg, Denmark
Dennis H. Lundtoft , Visual Analysis of People (VAP) Laboratory, Rendsburggade 14, 9000 Aalborg, Denmark
Thomas B. Moeslund , Visual Analysis of People (VAP) Laboratory, Rendsburggade 14, 9000 Aalborg, Denmark
Tanja L. Pedersen , Dept. of Communication and Psychology, Fredrik Bajers Vej 7, 9220 Aalborg, Denmark
Maria-Louise Klitgaard , Dept. of Communication and Psychology, Fredrik Bajers Vej 7, 9220 Aalborg, Denmark
Laura Petrini , Dept. of Communication and Psychology, Fredrik Bajers Vej 7, 9220 Aalborg, Denmark
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.
INDEX TERMS
Pain, Face, Histograms, Face recognition, Feature extraction, Spatiotemporal phenomena, Calibration,
CITATION
Ramin Irani, Kamal Nasrollahi, Marc O. Simon, Ciprian A. Corneanu, Sergio Escalera, Chris Bahnsen, Dennis H. Lundtoft, Thomas B. Moeslund, Tanja L. Pedersen, Maria-Louise Klitgaard, Laura Petrini, "Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition", 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), vol. 00, no. , pp. 88-95, 2015, doi:10.1109/CVPRW.2015.7301341
90 ms
(Ver 3.3 (11022016))