Hand Shape and 3D Pose Estimation Using Depth Data from a Single Cluttered Frame

This paper describes a method that, given an input image of a person signing a gesture in a cluttered scene, locates the gesturing arm, automatically detects and segments the hand and finally creates a ranked list of possible shape class, 3D pose orientation and full hand configuration parameters. The clutter-tolerant hand segmentation algorithm is based on depth data from a single image captured with a commercially available depth sensor, namely the

Kinect

TM

. Shape and 3D pose estimation is formulated as an image database retrieval method where given a segmented hand the best matches are extracted from a large database of synthetically generated hand images. Contrary to previous approaches this clutter-tolerant method is all-together: user-independent, automatically detects and segments the hand from a single image (no multi-view or motion cues employed) and provides estimation not only for the 3D pose orientation but also for the full hand articulation parameters. The performance of this approach is quantitatively and qualitatively evaluated on a dataset of real images of American Sign Language (ASL) handshapes.