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

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Exploiting Visual Quasi-Periodicity for Automated Chewing Event Detection Using Active Appearance Models and Support Vector Mach
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 embedding of the power spectra are employed to train a Support Vector Machine (SVM) binary classifier to detect chewing events. Experimental results yielded a crossvalidated percentage agreement of 93.4%, indicating that the proposed system provides an efficient approach to automated chewing detection.
Steven Cadavid, Mohamed Abdel-Mottaleb
Added 13 May 2010
Updated 13 May 2010
Type Conference
Year 2010
Where ICPR
Authors Steven Cadavid, Mohamed Abdel-Mottaleb
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