Gesture recognition approach for sign language using curvature scale space and hidden Markov model

The work presents a gesture recognition approach for sign language using curvature scale space (CSS) and hidden Markov model (HMM). First, we use the translation, scale and rotation-invariant CSS descriptor to characterize the hand shapes of gestures. Then, we propose a feature-preserving algorithm to allocate CSS features into a one-dimensional and fixed-sized feature vector for HMM since the CSS features are two-dimensional and the number of the extracted CSS features of each hand shape is not fixed. Finally, we apply the HMM to determine hand shape and trajectory transitions among the different hand shapes and trajectories of the gestures for sign language identification. Results show the proposed approach performs well for sign language recognition.