Title: Fast recognition and classification of static and dynamic signs for Persian sign language

Authors: Milad Moghaddam; Manoochehr Nahvi; Negin Pourmomtaz

Addresses: Department of Electrical Engineering, University of Guilan, Rasht, Guilan, Iran ' Department of Electrical Engineering, University of Guilan, Rasht, Guilan, Iran ' Department of Electrical Engineering, University of Guilan, Rasht, Guilan, Iran

Abstract: Sign Language (SL) is the most effective way for communication between deaf and hearing-impaired people. Since most non-deaf people are not familiar with SL, a vision-based translator/interpreter can be a very useful tool to enhance their communication. This paper presents a recognition system for Persian static and dynamic signs. The system is designed based on proposed modified non-linear kernel-based fast feature extraction methods, consisting of hybrid kernel principal component analysis and hybrid kernel discriminant analysis. For recognition of dynamic signs, the proposed feature extraction method is employed in association with spatio-temporal approach. The proposed methods are examined and compared with several existing feature extraction methods, including linear and non-linear kernel-based methods. The experiments indicate that our feature extraction methods significantly outperform other methods and reduce computational time while they achieve high recognition rates. Our simulations achieved a promising classification accuracy rate of 96.78% on static and 96.99% on dynamic signs, respectively.

Keywords: human-computer interaction; Persian sign language recognition; hand gestures; kernel-based feature extraction; static signs; dynamic signs.

DOI: 10.1504/IJCAT.2020.109341

International Journal of Computer Applications in Technology, 2020 Vol.63 No.3, pp.228 - 240

Received: 08 Mar 2019
Accepted: 11 Feb 2020
Published online: 03 Sep 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article