Teaching a Robot Sign Language using Vision-Based Hand Gesture Recognition

This paper presents a novel vision-based hand gesture recognition (HGR) and training system for a human-like robot hand. We implemented and trained a multiclass-SVM classifier and N-Dimensional DTW (ND-DTW) classifier for static posture recognition and dynamic gesture recognition. Training features were extracted from the raw gestures depth data captured by Leap Motion Controller. The experimental results show that multiclass SVM method has an average 98.25% recognition rates and the shortest run time when compared to k-NN and ANBC. For dynamic gestures, ND-DTW classifier displays a better performance than DHMM with an average 95.5% recognition rate and significantly shorter run time. In conclusion, the combination of SVMs and DTW proves the efficiency and high accuracy in proposed human-robot interaction system.