Facial Feature Tracking and Occlusion Processing in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language (ASL).  Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition.  One problem with detection of such grammatical indicators is occlusion recovery.  If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows.  We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion.

Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers, and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.

Below are links to five AVI video clips showing our system processing video of ASL signers.  The status bar (just under the video) shows which grammatical markers have been detected by the system for that frame.

Video Clips (videos will come soon)

Acknowledgements

We would like to thank Stan Sclaroff and Vassilis Athitsos for helping to provide the ASL videos used in this research.

Funding was provided by the National Science Foundation (IIS-0329009, IIS-0093367, IIS-9912573, EIA-0202067, and EIA-9809340) and the Office of Naval Research (N000140110444).