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