An Efficient Vision-Based Hand Beckon Perception for Physically Debilitated People using MCMC and HMM
DOI:
https://doi.org/10.51983/ajes-2015.4.1.1931Keywords:
Gesture recognition, Hidden Markov Model (HMM), Markov Chain Monte Carlo (MCMC), Human Computer Interaction (HCI), Artificial Neural Network (ANN)Abstract
Recognition of hand gestures has a significant impact on human society. It is a natural and intuitive way to provide the interaction between human and the computer. It provides touchless interaction and easy way to interact without any external devices. With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. Hidden Markov models (HMMs) and related models have become standard in statistics with applications in diverse areas. Markov chain Monte Carlo (MCMC) is great stuff. MCMC revitalized Bayesian inference and frequents inference about complex dependence. A high performance Artificial Neural Network (ANN) classifier is employed to improve the classification and accuracy.
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