Paper Info
Title
A probabilistic model for state sequence analysis in hidden Markov model for hand gesture recognition
Abstract
The role of gesture recognition is significant in areas like human-computer interaction, sign language, virtual reality, machine vision, etc. Among various gestures of the human body, hand gestures play a major role to communicate nonverbally with the computer. As the hand gesture is a continuous pattern with respect to time, the hidden Markov model (HMM) is found to be the most suitable pattern recognition tool, which can be modeled using the hand gesture parameters. The HMM considers the speeded up robust feature features of hand gesture and uses them to train and test the system. Conventionally, the Viterbi algorithm has been used for training process in HMM by discovering the shortest decoded path in the state diagram. The recursiveness of the Viterbi algorithm leads to computational complexity during the execution process. In order to reduce the complexity, the state sequence analysis approach is proposed for training the hand gesture model, which provides a better recognition rate and accuracy than that of the Viterbi algorithm. The performance of the proposed approach is explored in the context of pattern recognition with the Cambridge hand gesture data set.
Year
DOI
Venue
2019
10.1111/coin.12188
COMPUTATIONAL INTELLIGENCE
Keywords
Field
DocType
hand gesture recognition,HCI,hidden Markov model,pattern recognition,SSA,Viterbi algorithm
Pattern recognition,State sequence,Computer science,Gesture recognition,Speech recognition,Artificial intelligence,Statistical model,Hidden Markov model,Viterbi algorithm
Journal
Volume
Issue
ISSN
35.0
1.0
0824-7935
Citations 
PageRank 
References 
0
0.34
14
Authors
2
Name
Order
Citations
PageRank
K. Martin Sagayam1141.64
D. Jude Hemanth211922.74