Facial expressions in American sign language: Tracking and recognition

This paper presents work towards recognizing facial expressions that are used in sign language communication. Facial features are tracked to effectively capture temporal visual cues on the signersʹ face during signing. Face shape constraints are used for robust tracking within a Bayesian framework. The constraints are specified through a set of face shape subspaces learned by Probabilistic Principal Component Analysis (PPCA). An update scheme is also used to adapt to persons with different face shapes. Two tracking algorithms are presented, which differ in the way the face shape constraints are enforced. The results show that the proposed trackers can track facial features with large head motions, substantial facial deformations, and temporary facial occlusions by hand. The tracked results are input to a recognition system comprising Hidden Markov Models (HMM) and a support vector machine (SVM) to recognize six isolated facial expressions representing grammatical markers in American sign language (ASL). Tracking error of less than four pixels (on 640×480 videos) was obtained with probability greater than 90%; in comparison the KLT tracker yielded this accuracy with 76% probability. Recognition accuracy obtained for ASL facial expressions was 91.76% in person dependent tests and 87.71% in person independent tests.