Hand Gesture Recognition Using Object Based Key Frame Selection

Author

Rokade, Ulka S. ; Doye, Dharmpal ; Kokare, Manesh

Author_Institution

P.G. Moze Coll. of Eng., Pune, India

fYear

2009

fDate

7-9 March 2009

Firstpage

288

Lastpage

291

Abstract

The sign language recognition is the most popular research area involving computer vision, pattern recognition and image processing. It enhances communication capabilities of the mute person. In this paper, we present an object based key frame selection. Hausdorff distance and Euclidean distance are used for shape similarity for hand gesture recognition. We proposed the use of nonlinear time alignment model with key frame selection facility and gesture trajectory features for hand gesture recognition. Experimental results demonstrate the effectiveness of our proposed scheme for recognizing American Sign Language.

Keywords

computer vision; feature extraction; gesture recognition; image representation; image sequences; object recognition; American Sign Language; Euclidean distance; Hausdorff distance; communication capability; computer vision; gesture trajectory feature; hand gesture recognition; image processing; mute person; nonlinear time alignment model; object based key frame selection; pattern recognition; shape similarity; Automata; Databases; Digital images; Handicapped aids; Hidden Markov models; Image recognition; Pattern recognition; Shape measurement; Video sequences; Vocabulary; Dynamic time warping; Finite state machine; Sign language; Trajectory feature;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Image Processing, 2009 International Conference on

Conference_Location

Bangkok

Print_ISBN

978-0-7695-3565-4

Type

conf

DOI

10.1109/ICDIP.2009.74

Filename

5190579