Emotion Detection
A sample video demontrating software that detect emotional states in a realtime manner for mobile apps, desktop systems and interactive experiences.

This technique currently uses an pattern recogntion to identify shapes within a image field using Viola and Jones Open CV Haar-like features application [1], [2],[3] and a “feret” database [4] of facial image and support vector machine (LibSVM) [3] to classify the capture of the camera view field and identify if a face exists. The system processses the detected faces using an elastic bunch graph mapping technique

that is trained to determine facial expressions. These facial expressions are graphed on a sliding scale to match the distance from a target emotion graph, thus giving an approx-imate determination of the users mood.

Pensyl, William Russell; Song Shuli Lily; Dias, Walson; Huang, Walter Yucheng Walter; Odell, Conor; Zhang, Yifan Henry

1. Viola, P., & Jones, M. (2001). Robust real-time object detection. Paper presented at the Second International Workshop on Theories of Visual Modelling Learning, Computing, and Sampling

2. Bradski, G. and Kaehler, A., (2008). Learning OpenCV. OReilly.

3. Burges, C. J.C., (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121-167

4. http://www.nist.gov/
huma nid/colorferet

5. Wiskott, L.; Fellous, J.-M.; Kuiger, N.; von der Malsburg, C. (1997) Face recognition by elastic bunch graph matching, Pattern Analysis and Machine Intelligence, IEEE Transactions on Machine Intelligence, Volume: 19 Issue:7 Pages 775 - 779

6. Bronstein, A. M.; Bronstein, M. M., and Kimmel, R. (2005). "Three-dimensional face recognition". International Journal of Computer Vision (IJCV) 64 (1): 5–30

7. Ekman, P., (1999), "Basic Emotions", in Dalgleish, T; Power, M, Handbook of Cognition and Emotion, Sussex, UK: John Wiley & Sons, http://www.paulekman.com/wp-content/uploads/2009/02/Basic-Emotions.pdf