Spatial and Temporal Pyramids for Grammatical Expression Recognition of American Sign Language

摘要

Given that sign language is used as a primary means of communication by as many as two million deaf individuals in the U.S. and as augmentative communication by hearing individuals with a variety of disabilities, the development of robust, real-time sign language recognition technologies would be a major step forward in making computers equally accessible to everyone. However, most research in the field of sign language recognition has focused on the manual component of signs, despite the fact that there is critical grammatical information expressed through facial expressions and head gestures.We propose a novel framework for robust tracking and analysis of facial expression and head gestures, with an application to sign language recognition. We then apply it to recognition with excellent accuracy (≥ 95%) of two classes of grammatical expressions, namely wh-questions and negative expressions. Our method is signer-independent and builds on the popular "bag-of-words" model, utilizing spatial pyramids to model facial appearance and temporal pyramids to represent patterns of head pose changes.