Indian sign language recognition using Krawtchouk moment-based local features

In this paper, Krawtchouk moment-based shape features at lower orders are proposed for Indian sign language (ISL) recognition system which gives local information about the shape from a specific region of interest. The shape recognition capability of Krawtchouk moment-based local features is verifie...

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Bibliographic Details
Published inThe imaging science journal Vol. 65; no. 3; pp. 171 - 179
Main Authors Kaur, Bineet, Joshi, Garima, Vig, Renu
Format Journal Article
LanguageEnglish
Published Taylor & Francis 03.04.2017
Subjects
Correlation-based feature selection (CFS)
human-computer interaction (HCI)
Indian sign language (ISL)
Krawtchouk moments
local features
orthogonal moments
region of interest (ROI)
shape recognition
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Summary:In this paper, Krawtchouk moment-based shape features at lower orders are proposed for Indian sign language (ISL) recognition system which gives local information about the shape from a specific region of interest. The shape recognition capability of Krawtchouk moment-based local features is verified on two databases: the standard Jochen Triesch's database and 26 ISL alphabets which are collected from 72 different subjects, with variations in position, scale and rotation. Feature selection is performed to minimise redundancy. The effect of order and feature dimensionality for different classifiers is studied. Results show that Krawtchouk moment-based local features are found to exhibit user, scale, rotation and translation invariance. Moreover, they have shape identification capability.
ISSN:1368-2199
1743-131X
DOI:10.1080/13682199.2017.1311524