An approach to recognizing human hand gestures from a monocular temporal sequence of images is presented. Of particular concern is the representation and recognition of hand movements that are used in single handed American Sign Language (ASL). The approach exploits previous linguistic analysis of manual languages that decompose dynamic gestures into their static and dynamic components. The first level of decomposition is in terms of three sets of primitives, hand shape, location and movement. Further levels of decomposition involve the lexical and sentence levels and are part of our plan for future work. We propose and subsequently demonstrate that given a monocular gesture sequence, kinematic features can be recovered from the apparent motion that provide distinctive signatures for 14 primitive movements of ASL. The approach has been implemented in software and evaluated on a database of 592 gesture sequences with an overall recognition rate of 86.00% for fully automated processing and 97.13% for manually initialized processing.
REF uses kinematic features to identify American sign language.
Hand Gesture Recognition within a Linguistics-Based Framework
In Proc. ECCV
Computer Science