1. Image-based stress recognition from a model-based tracking system Sundara Venkataraman (sundara@paul)

2. Motivation Stress recognition from faces has a lot of applications Human-computer Interaction Security Problem has not been explored Our approach : Tracking faces + recognition from tracking data

3. Face Tracking Generic face model is fit to a given subject’s face Involves marking out the contour, eyes, nose and mouth Each of these parts are fit separately to get an accurate fit of a subject’s face Model incorporates framework for eyebrow movements, lip deformations and jaw movements

4. Face Tracking Tracking is based on statistical cue integration Cues are edges, point trackers and optical flow The cues are integrated statistically using a maximum likelihood estimation

5. Stress Recognition Training/Testing data Tracking data for about 13 subjects with high/low stress situations were used for training/recognition Used HTK for HMM training and testing Issues in using Dynamic Bayesian Network with BNT

6. Results 5 states in the HMM for both data splits 75% - 25 % split between training and test data gave 100% recognition. 50% - 50% split between training and test data gave 92 % recognition accuracy.