Optical computer recognition of facial expressions associated with stress induced by performance demands.
Application of computer vision to track changes in human facial expressions during long-duration spaceflight may be a useful way to unobtrusively detect the presence of stress during critical operations. To develop such an approach, we applied optical computer recognition (OCR) algorithms for detecting facial changes during performance while people experienced both low- and high-stressor performance demands. Workload and social feedback were used to vary performance stress in 60 healthy adults (29 men, 31 women; mean age 30 yr). High-stressor scenarios involved more difficult performance tasks, negative social feedback, and greater time pressure relative to low workload scenarios. Stress reactions were tracked using self-report ratings, salivary cortisol, and heart rate. Subjects also completed personality, mood, and alexithymia questionnaires. To bootstrap development of the OCR algorithm, we had a human observer, blind to stressor condition, identify the expressive elements of the face of people undergoing high- vs. low-stressor performance. Different sets of videos of subjects' faces during performance conditions were used for OCR algorithm training. Subjective ratings of stress, task difficulty, effort required, frustration, and negative mood were significantly increased during high-stressor performance bouts relative to low-stressor bouts (all p < 0.01). The OCR algorithm was refined to provide robust 3-d tracking of facial expressions during head movement. Movements of eyebrows and asymmetries in the mouth were extracted. These parameters are being used in a Hidden Markov model to identify high- and low-stressor conditions. Preliminary results suggest that an OCR algorithm using mouth and eyebrow regions has the potential to discriminate high- from low-stressor performance bouts in 75-88% of subjects. The validity of the workload paradigm to induce differential levels of stress in facial expressions was established. The paradigm also provided the basic stress-related facial expressions required to establish a prototypical OCR algorithm to detect such changes. Efforts are underway to further improve the OCR algorithm by adding facial touching and automating application of the deformable masks and OCR algorithms to video footage of the moving faces as a prelude to blind validation of the automated approach.
Dinges
David F
DF
Unit for Experimental Psychiatry, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA. dinges@mail.med.upenn.edu
Rider
Robert L
RL
Dorrian
Jillian
J
McGlinchey
Eleanor L
EL
Rogers
Naomi L
NL
Cizman
Ziga
Z
Goldenstein
Siome K
SK
Vogler
Christian
C
Venkataraman
Sundara
S
Metaxas
Dimitris N
DN
D000328:Adult; D000465:Algorithms; D018480:Astronauts / Q000523:psychology*; D035841:Behavioral Research / Q000379:methods*; D003936:Diagnosis, Computer-Assisted; D004644:Emotions / Q000502:physiology; D005149:Facial Expression; D005246:Feedback; D005260:Female; D006801:Humans; D008297:Male; D008603:Mental Health; D010363:Pattern Recognition, Automated; D013315:Stress, Psychological / Q000175:diagnosis*; D011795:Surveys and Questionnaires; D013647:Task Performance and Analysis; D013997:Time Factors