Classification performance of different classifiers on head gestures and facial expressions

In this study, we analyze head gestures and facial expressions in face video streams. Facial landmark trajectories, which are the tracked coordinates of the landmarks in x and y directions, are extracted via an automatic and robust facial landmark tracking algorithm. Both raw features and features intuitively selected to reflect mimics are used. Examples of the latter category are mutual distances, angles and ratios of landmarks. The analyzer exploits the trajectories of facial landmark features during the course of the head and facial gesture. The feature trajectories are handled both via matrix subspace methods NMF, ICA, and via dynamic classifiers such as HMM, HCRF. The classification results on the seven different head and face gesture classes are satisfactory.