Indian sign language recognition using Krawtchouk moment-based local features. (3rd April 2017)

• Record Type: Journal Article
• Authors: Kaur, Bineet; Joshi, Garima; Vig, Renu
• Is Part Of: Imaging science journal. Volume 65:Number 3(2017)
• Page Start: 171
• Page End: 179
• Publication Date: 2017-04-03
• 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; Imaging systems -- Periodicals; 621.36705
• DOI: 10.1080/13682199.2017.1311524
• Languages: English
• ISSNs: 1368-2199

Abstract

ABSTRACT: 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.