In this chapter, the problem of gesture recognition in the context of human computer interaction is considered. Several classifiers based on different approaches such as neural network (NN), support vector machine (SVM), hidden Markov model (HMM), deep neural network (DNN), and dynamic time warping (DTW) are used to build the gesture models. The performance of each methodology is evaluated considering different users performing the gestures. This performance analysis is required as the users perform gestures in a personalized way and with different velocity. So the problems concerning the different lengths of the gesture in terms of number of frames, the variability in its representation, and the generalization ability of the classifiers have been analyzed.
Part of the book: Motion Tracking and Gesture Recognition