Self co-articulation detection and trajectory guided recognition for dynamic hand gestures
Hand gestures are a natural way of communication among humans in everyday life. Presence of spatiotemporal variations and unwanted movements within a gesture called self co-articulation makes the segmentation a challenging task. The study reveals that the self co-articulation may be used as one of the feature to enhance the performance of hand gesture recognition system. It was detected from the gesture trajectory by addition of speed information along with the pause in the gesture spotting phase. Moreover, a new set of novel features in the feature extraction stage was used such as position of the hand, self co-articulated features, ratio and distance features. The ANN and SVM were used to develop two independent models using new set of features as input. The models based on CRF and HCRF was used to develop the baseline system for the present study. The experimental results suggest that the proposed new set of features provides improvement in terms of accuracy using ANN (7.48%) and SVM (9.38%) based models as compared with baseline CRF based model. There are also significant improvements in the performances of both ANN (2.08%) and SVM (3.98%) based models as compared with HCRF based model.