Curve Matching from the view of manifold for sign language recognition

Abstract

Sign language recognition is a challenging task due to the complex action variations and the large vocabulary set. Generally, sign language conveys meaning through multichannel information like trajectory, hand posture and facial expression simultaneously. Obviously, trajectories of sign words play an important role for sign language recognition. Although the multichannel features are helpful for sign representation, this paper only focuses on the trajectory aspect. A method of curve matching based on manifold analysis is proposed to recognize isolated sign language word with 3D trajectory captured by Kinect. From the view of manifold, the main structure of the curve is found by the intrinsic linear segments, which are characterized by some geometric features. Then the matching between curves is transformed into the matching between two sets of sequential linear segments. The performance of the proposed curve matching strategy is evaluated on two different sign language datasets. Our method achieves a top-1 recognition rate of 78.3% and 61.4% in a 370 daily words dataset and a large dataset containing 1000 vocabularies.