title: Automatic decoding of facial movements reveals deceptive pain expressions.
abstract: In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1-3]. Two motor pathways control facial movement [4-7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8-11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55%. However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.
authors.affiliation: Institute for Neural Computation, University of California, 9500 Gilman Drive, MC 0440, La Jolla, San Diego, CA 92093-0440, USA. Electronic address: mbartlett@ucsd.edu.
authors.forename: Marian Stewart
authors.initials: MS
authors.lastname: Bartlett
authors.affiliation: Institute for Neural Computation, University of California, 9500 Gilman Drive, MC 0440, La Jolla, San Diego, CA 92093-0440, USA.
authors.forename: Gwen C
authors.initials: GC
authors.lastname: Littlewort
authors.affiliation: Department of Communication, University at Buffalo, State University of New York, Baldy Hall, Buffalo, NY 14260-1020, USA.
authors.forename: Mark G
authors.initials: MG
authors.lastname: Frank
authors.affiliation: Dr. Erick Jackman Institute of Child Study, University of Toronto, 45 Walmer Road, Toronto, ON M5R 2X2, Canada. Electronic address: kang.lee@utoronto.ca.
authors.forename: Kang
authors.initials: K
authors.lastname: Lee
mesh_terms: D001185:Artificial Intelligence; D005149:Facial Expression; D006801:Humans; D010146:Pain; D010363:Pattern Recognition, Automated