Comparison study of Hidden Markov Model gesture recognition using fixed state and variable state

Author

Gaus, Yona Falinie A. ; Wong, Francis ; Teo, Kok Lay ; Chin, Richard ; Porle, Rosalyn R. ; Lim Pei Yi ; Chekima, Ali

Author_Institution

Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah, Kota Kinabalu, Malaysia

fYear

2013

fDate

8-10 Oct. 2013

Firstpage

150

Lastpage

155

Abstract

This paper presents a method of gesture recognition using Hidden Markov Model (HMM). Gesture itself is based on the movement of each right hand (RH) and left hand (LH), which represents the word intended by the signer. The feature vector selected, gesture path, hand distance and hand orientations are obtained from RH and LH then trained using HMM to produce the respective gesture class. While training, in handling HMM state, we introduce fixed state and variable state, where in fixed state, the numbers of state is generally fixed for all gestures and while the number of state in variable state is determined by the movement of the gesture. It was found that fixed state gave the highest rate of recognition achieving 83.1%.

Keywords

gesture recognition; hidden Markov models; HMM; fixed state; gesture path; hand distance; hand orientations; hidden Markov model gesture recognition; variable state; Adaptation models; Data models; Hidden Markov models; Image recognition; Markov processes; Vectors; HMM; feature vector; fixed state; gesture path; hand distance; hand orientation; variable state;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Image Processing Applications (ICSIPA), 2013 IEEE International Conference on

Conference_Location

Melaka

Print_ISBN

978-1-4799-0267-5

Type

conf

DOI

10.1109/ICSIPA.2013.6707994

Filename

6707994