Real-Time Depth-Camera Based Hand Tracking for ASL Recognition

Accurate real-time depth camera-based tracking of limbs, fingers and faces would be of great use to the field of Sign Language Recognition (SLR). While aspects of depth-based tracking have been applied to SLR, technological limitations have previously forced trade-offs between the resolution necessary to track finger positions and the field of view necessary to track the signer's body. Only recently, with improvements in cameras and computing power, have algorithms been developed which boast the capability of maintaining accurate finger tracking over an appropriately sized volume of space. In this paper, we employ the publicly available Sphere-Mesh [1] hand tracking algorithm to collect and recognize ASL handshapes. In doing so, we demonstrate recognition rates comparable to other state of the art handshape classifiers using simple naíve Bayesian classifiers that can run in real-time.