Program
Introduction
Facial Action Coding System (FACS)
Computational models for Facial Expression Analysis
- Registration
- Features
- Supervised Facial Expression Analysis
- Unsupervised Facial Expression Analysis
Applications to:
- Clinical depression
- Pain Assessment
- Facial expression as biometrics
- Personality and facial expression
Conclusions and open problems
Target participants
The tutorial is targeted to researchers or practitioners that use video to analyze facial behavior. The tutorial will be self-contained. Basic knowledge on computer vision will be required to understand some of the methods, but in the second part of the tutorial (application area) no knowledge on computer vision is required.
Relevant literature
Alghowinem,
S., Goecke,
R., Cohn, J. F., Wagner, M., Parker, G., & Breakspear,
M. (2015). Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses..
Image and Vision Computing, 32(10), 641–647.
Hammal,
Z. & Cohn, J.F. (2014). Towards
multimodal pain assessment for research and clinical use. Proceedings
of the 2014 Workshop on Road-mapping the Future of Multimodal
Interaction Research including Business Opportunities and
Challenges. Istanbul,
Turkey
Girard,
J. M., Cohn, J. F., Sayette,
M. A., Jeni,
L. A., & De la Torre,
F. (2015). How
much training data for facial action unit detection? Proceedings
of the IEEE International Conference on Automatic Face and Gesture
Recognition, Ljubljana, Slovenia
De
la Torre, F., Chu, W.-S., Xiong,
X., Vicentey,
F., Dingy, X., & Cohn, J. F. (2015). IntraFace. Proceedings
of the IEEE International Conference on Automatic Face and Gesture
Recognition, Ljubljan,
Slovenia.
Girard,
J. M., Cohn, J. F., Sayette,
M. A., Jeni,
L., & De la Torre,
F. (2014). Spontaneous
facial expression can be measured automatically. Behavior
Research Methods.
Estimating
smile intensity: A better way
Jeffrey
M. Girard, Jeffrey F. Cohn and Fernando De la Torre
Pattern
Recognition Letters,
2014.
Automated
Face Analysis for Affective Computing
J.
F. Cohn and F. De la Torre
The
Oxford Handbook of Affective Computing,
2014.
Max-Margin
Early Event Detectors
M.
Hoai and F. De la Torre
International
Journal of Computer Vision (IJCV),
vol. 107, issue 2, pp. 191-202, 2014
Facing
imbalanced data recommendations for the use of performance
metrics
L.
A. Jeni, J. F. Cohn and F. De la Torre
Affective
Computing and Intelligent Interaction (ACII),
2013.
Selective
Transfer Machine for Personalized Facial Action Unit Detection
W.-S.
Chu, F. De la Torre and J. F. Cohn
IEEE
Conference on Computer Vision and Pattern Recognition (CVPR),
2013.
Unsupervised
Temporal Commonality Discovery
W.-S.
Chu, F. Zhou and F. De la Torre
European
Conference on Computer Vision (ECCV),
2012
FAST-FACS:
A Computer-Assisted System to Increase Speed and Reliability of
Manual FACS Coding
F.
De la Torre, T. Simon, Z. Ambadar and J. F. Cohn
Affective
Computing and Intelligent Interaction (ACII),
2011.
Facial
Expression Analysis
F. De la Torre and J. F.
Cohn
Guide to Visual Analysis of Humans: Looking at
People, Springer, 2011.
Dynamic
Cascades with Bidirectional Bootstrapping
for
Spontaneous Facial Action Unit Detection
Y.
Zhu, F. De la Torre, J. Cohn and Y. Zhang
IEEE
Transactions on Affective Computing,
vol. 2, issue 2, pp. 79-91, 2011.
Detecting
Depression from Facial Actions and Vocal Prosody
J.
F. Cohn, T. Simon, I. Matthews, Y. Yang, M. H. Nguyen, M. Tejera, F.
Zhou and F. De la Torre
Affective
Computing and Intelligent Interaction (ACII),
September 2009.