Spatiotemporal-Boosted DCT Features for Head and Face Gesture Analysis

Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial landmark points are detected and tracked over successive video frames using a robust method based on subspace regularization, Kalman prediction and refinement. The trajectories (time series) of facial landmark positions during the course of the head gesture or facial expression are organized in a spatiotemporal matrix and discriminative features are extracted from the trajectory matrix. Alternatively, appearance based features are extracted from DCT coefficients of several face patches. Finally Adaboost algorithm is performed to learn a set of discriminating spatiotemporal DCT features for face and head gesture (FHG) classification. We report the classification results obtained by using the Support Vector Machines (SVM) on the outputs of the features learned by Adaboost. We achieve 94.04% subject independent classification performance over seven FHG. 1 2