Abstract—Hand gesture became second language that complements almost many speeches, encounters, lectures, as well as friends chatting even in computer chatting they may gesturing to each other since it is a rotted habit in our behavior, we can notice that even if someone was sitting alone and thinking; he will continue gesturing during his meditation, however, the imitating of this natural behavior is an important issue for transferring this behavior to the human made machines and the intuitive interface will not be changed as compared to human-human communication, in this paper, we have applied a novel approach for recognizing the hand gesture and to maximize the level of unrestricted communication by solving the problem of rotation invariance that matters, we have employed a Gaussian bivariate likelihood function for hand modeling and features fitting and to produce uniform features that can be a reference for gesture database, we have achieved remarkable recognition percentages using 20 different gestures with a high speed recognition time, our system can be used for real time applications in which the time factor is important issue for the success of such systems, we have made a comparative study with some other known gesture algorithms as well.
Index Terms—Gesture recognition, gaussian mulivariate function, gaussian bivariate funciotn, template matching, orientation histogram, elastic graph, fuzzy C-mean, euclidian distance.
Authors are with Banaras Hindu University, Varanasi, Uttar Pradesh, India (email: mmwaeli@gmail.com; pkmisra@gmail.com).
Cite: Mokhtar M. Hasan and Pramod K. Mishra, "Robust Gesture Recognition Using Gaussian Distribution for Features Fitting," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 266-273, 2012.