Indian sign language recognition using Krawtchouk moment-based local features

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.

KEYWORDS:

• 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

Disclosure statement

No potential conflict of interest was reported by the authors.