Cited by 425Emotion and Mood RecognitionFace recognition and analysisFace Recognition and Perception

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Measuring facial expressions by computer image analysis

Bartlett, Marian Stewart, Hager, Joseph C., Ekman, Paul, Sejnowski, Terrence J.·1999·Psychophysiology

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Abstract

Facial expressions provide an important behavioral measure for the study of emotion, cognitive processes, and social interaction. The Facial Action Coding System (Ekman & Friesen, 1978) is an objective method for quantifying facial movement in terms of component actions. We applied computer image analysis to the problem of automatically detecting facial actions in sequences of images. Three approaches were compared: holistic spatial analysis, explicit measurement of features such as wrinkles, and estimation of motion flow fields. The three methods were combined in a hybrid system that classified six upper facial actions with 91% accuracy. The hybrid system outperformed human nonexperts on this task and performed as well as highly trained experts. An automated system would make facial expression measurement more widely accessible as a research tool in behavioral science and investigations of the neural substrates of emotion.

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Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
Eigenfaces for Recognition
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,798
Determining Optical Flow
Horn, B. K. P., & Schunck, B. G. (1981) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.965761 · Cited by 7,443
Autonomic nervous system activity distinguishes among emotions
Ekman, P., Levenson, R. W., & Friesen, W. V. (1983) · Science · doi:10.1126/science.6612338 · Cited by 2,692
Self-citation
Face recognition: features versus templates
Brunelli, R., & Poggio, T. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.254061 · Cited by 2,452
A differential neural response in the human amygdala to fearful and happy facial expressions
Morris, J. S., Frith, C. D., Perrett, D. I., Rowland, D., Young, A. W., Calder, A. J., & Dolan, R. J. (1996) · Nature · doi:10.1038/383812a0 · Cited by 2,013
What the Face RevealsBasic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)
Ekman, P., & Rosenberg, E. L. (2005) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195179644.001.0001 · Cited by 1,971
Self-citation
Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression
Breiter, H. C., Etcoff, N. L., Whalen, P. J., Kennedy, W. A., Rauch, S. L., Buckner, R. L., Strauss, M. M., Hyman, S. E., & Rosen, B. R. (1996) · Neuron · doi:10.1016/s0896-6273(00)80219-6 · Cited by 1,711
Approach-withdrawal and cerebral asymmetry: Emotional expression and brain physiology: I.
Davidson, R. J., Ekman, P., Saron, C. D., Senulis, J. A., & Friesen, W. V. (1990) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.58.2.330 · Cited by 1,684
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Beymer, D., Shashua, A., & Poggio, T. ~ 1993 ! . Example based image analysis and synthesis ~ AI Memo No. 1431 ! . Cambridge, MA: MIT Press.
Cottrell, G. W., & Fleming, M. K. ~ 1990 ! . Face recognition using unsupervised feature extraction. Proceedings of the International Neural Network Conference ~ pp. 322-325 ! . Paris: Kluwer.
Cottrell, G., & Metcalfe, J. ~ 1991 ! . EMPATH: Face, gender and emotion recognition using holons. In R. P. Lippman, J. Moody, & D. Touretzky ~ Eds. ! , Advances in neural information processing systems ~ Vol. 3, pp. 564-571 ! . San Mateo, CA: Morgan & Kaufman.
Ekman, P. ~ Ed. ! . ~ 1982a ! . Emotion in the human face ~ 2nd ed. ! . New York: Cambridge University Press.
Ekman, P., & Friesen, W. ~ 1978 ! . Facial Action Coding System: A technique for the measurement of facial movement. Palo Alto, CA: Consulting Psychologists Press.
Ekman, P., Huang, T., Sejnowski, T., & Hager, J. ~ 1992 ! . Final Report to NSF of the Planning Workshop on Facial Expression Understanding @ on-line # . Available: http: 00 alt.xmission.com 0 nirc1 0 Research 0 research.html
Hager, J., & Ekman, P. ~ 1995 ! . The essential behavioral science of the face and gesture that computer scientists need to know. In M. Bichsel ~ Ed. ! , Proceedings of the International Workshop on Automatic Face and Gesture Recognition ~ pp. 7-11 ! . Available: http: 00 alt.xmission.com 0 nirc1 0 Research 0 research.html
Himer, W., Schneider, F., Kost, G., & Heimann, H. ~ 1991 ! . Computerbased analysis of facial action: A new approach. Journal of Psychophysiology , 5 , 189-195.
Jain, R., Kasturi, R., & Schunk, B. G. ~ 1995 ! . Machine vision. New York: McGraw-Hill.
Tukey, J. W. ~ 1958 ! . Bias and confidence in not-quite large samples. Annals of Mathematical Statistics , 29 , 614.
Zajonc, R. B. ~ 1984 ! . The interaction of affect and cognition. In K. Scherer & P. Ekman ~ Eds. ! , Approaches to emotion ~ pp. 239-246 ! . Hillsdale, NJ: Erlbaum.

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Authors: Bartlett, M. S., Hager, J. C., Ekman, P., & Sejnowski, T. J.
Affiliations: Salk Institute for Biological Studies (US)
University of California, San Francisco (US)
Corresponding author: Bartlett, Marian Stewart
Venue: Psychophysiology
Publisher: Wiley
Published: 1999-03
Volume: 36
Issue: 2
Pages: 253-263
ISSN: 0048-5772 (ISSN-L)
1469-8986
DOI: 10.1017/s0048577299971664
OpenAlex: W2007431552
Cited by: 424
References: 47
Citations by year: 2026: 7 · 2025: 8 · 2024: 13 · 2023: 11 · 2022: 34 · 2021: 23 · 2020: 16 · 2019: 23 · 2018: 14 · 2017: 20 · 2016: 14 · 2015: 17 · 2014: 19 · 2013: 12 · 2012: 12
FWCI: 19.99
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor, 1479 d)
Locations: Psychophysiology (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
Keywords: facial expressions, facial action units, facial action coding system, computer image analysis, wrinkles, hybrid classification system, emotion measurement
MeSH terms: Adult, Cues, Facial Expression, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Databases as Topic

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