@article{(Open Science Index):https://publications.waset.org/pdf/17283,
	  title     = {Vision Based Hand Gesture Recognition Using Generative and Discriminative Stochastic Models},
	  author    = {Mahmoud Elmezain and  Samar El-shinawy},
	  country	= {},
	  institution	= {},
	  abstract     = {Many approaches to pattern recognition are founded on probability theory, and can be broadly characterized as either generative
or discriminative according to whether or not the distribution of the image features. Generative and discriminative models have
very different characteristics, as well as complementary strengths and weaknesses. In this paper, we study these models to recognize the patterns of alphabet characters (A-Z) and numbers (0-9). To handle isolated pattern, generative model as Hidden Markov Model (HMM) and discriminative models like Conditional Random Field (CRF), Hidden Conditional Random Field (HCRF) and Latent-Dynamic Conditional Random Field (LDCRF) with different number of window size are applied on extracted pattern features. The gesture recognition rate is improved initially as the window size increase, but degrades as window size increase further. Experimental results show that the LDCRF is the best in terms of results than CRF, HCRF and HMM at window size equal 4. Additionally, our results show that; an overall recognition rates are 91.52%, 95.28%, 96.94% and 98.05% for CRF,
HCRF, HMM and LDCRF respectively.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {11},
	  year      = {2013},
	  pages     = {1405 - 1411},
	  ee        = {https://publications.waset.org/pdf/17283},
	  url   	= {https://publications.waset.org/vol/83},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 83, 2013},