Shape, texture and local movement hand gesture features for Indian Sign Language recognition

This paper proposes an automatic gesture recognition approach for Indian Sign Language (ISL). Indian sign language uses both hands to represent each alphabet. We propose an approach which addresses local-global ambiguity identification, inter-class variability enhancement for each hand gesture. Hand region is segmented and detected by YCbCr skin color model reference. The shape, texture and finger features of each hand are extracted using Principle Curvature Based Region (PCBR) detector, Wavelet Packet Decomposition (WPD-2) and complexity defects algorithms respectively for hand posture recognition process. To classify each hand posture, multi class non linear support vector machines (SVM) is used, for which a recognition rate of 91.3% is achieved. Dynamic gestures are classified using Dynamic Time Warping (DTW) with the trajectory feature vector with 86.3% recognition rate. The performance of the proposed approach is analyzed with well known classifiers like SVM, KNN & DTW. Experimental results are compared with the conventional and existing algorithms to prove the better efficiency of the proposed approach.