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

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.