Automatic Decoding of Facial Movement Reveals Deceptive Pain Expressions
by
, , ,
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.
WP-02
Date of Publication:
30 November -0001
30 November -0001
Year of Publication:
2014
2014
@article{sensecare:517,
- author = {Bartlett MS and Littlewort GC and Frank MG and Lee K},
- title = {Automatic Decoding of Facial Movement Reveals Deceptive Pain Expressions},
- year = {2014},
- date = {November 30, -0001},
Bartlett MS and Littlewort GC and Frank MG and Lee K 2014 Automatic Decoding of Facial Movement Reveals Deceptive Pain Expressions November 30, -0001
Click on the link under to view document:
Workpackages
WP2 Affective Computing (AC) & Machine Learning
Related Articles
Document
Auto Detection of Chronic Pain Expressions
Min S. H. Aung, Sebastian Kaltwang, Bernardino Romera-Paredes, Brais Martinez, Aneesha Singh, Matteo Cella, Michel Valstar, Hongying Meng, Andrew Kemp, Moshen Shafizadeh, Aaron C. Elkins, Natalie Kanakam, Amschel de Rothschild de, Nick Tyler, Paul J. Watson, Amanda C. de C. Williams de, Maja Pantic, Nadia Bianchi-Berthouze
Document
Automatic Detection of Engagement Kinect
Hamed Monkaresi, Nigel Bosch, Rafael Calvo, Sidney D'Mello
Document
Remarks on SVM-based emotion recognition from multi-modal bio-potential signals
K. Takahashi
Document
ACM Physiological Computing and BCI
Erin Treacy Solovey, Daniel Afergan, Evan M. Peck, Samuel W. Hincks, Robert J. K. Jacob
Document
Emotion recognition system using short-term monitoring of physiological signals
K. H. Kim, et al et