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Exploiting Visual Quasi-periodicity for Automated Chewing Event Detection Using Active Appearance Models and Support Vector Machines

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2 Author(s)
Cadavid, S. ; Dept. of Electr. & Comput. Eng., Univ. of Miami, Coral Gables, FL, USA ; Abdel-Mottaleb, M.

We present a method that automatically detects chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject's face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality reduction via spectral regression. The low-dimensional representations of the power spectra are employed to train a Support Vector Machine (SVM) binary classifier to detect chewing events. Experimental results yielded a cross validated percentage agreement of 93.4%, indicating that the proposed system provides an efficient approach to automated chewing detection.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010