Software-based Malaysian sign language recognition

Farrah Wong, and Ali Chekima, and Faysal Ezwen Jupirin, and Yona Falinie Abdul Gaus, and Sainarayanan Gopala, and Wan Mahani Abdullah, (2013) Software-based Malaysian sign language recognition. Springer, Berlin, Heidelberg, pp. 297-306.

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Abstract

This work presents the development of a software-based Malaysian Sign Language recognition system using Hidden Markov Model. Ninety different gestures are used and tested in this system. Skin segmentation based on YCbCr colour space is implemented in the sign gesture videos to separate the face and hands from the background. The feature vector of sign gesture is represented by chain code, distance between face and hands and tilting orientation of hands. This work has achieved recognition rate of 72.22%.

Item Type: Book
Uncontrolled Keywords: Hidden Markov Model, gestures, skin segmentation
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: Unnamed user with email storage.bpmlib@ums.edu.my
Date Deposited: 28 Nov 2016 05:16
Last Modified: 11 Oct 2017 06:52
URI: http://eprints.ums.edu.my/id/eprint/15017

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