Volume 4, Issue 1 (3-2012)                   IJICTR 2012, 4(1): 21-28 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Moghaddam M, Nahvi M, P.R. Hasanzadeh R. Static Persian Sign Language Recognition Using Kernel-Based Feature Extraction . IJICTR. 2012; 4 (1) :21-28
URL: http://ijict.itrc.ac.ir/article-1-192-en.html
1- DSP Research Lab, Department of Electrical Engineering, University of Guilan Rasht, Iran
2- DSP Research Lab, Department of Electrical Engineering University of Guilan Rasht, Iran
Abstract:   (2098 Views)

The most effective way for deaf people communication is sign language. Since most people are not familiar with this language, there is a requirement for a sign language translator system. This would be a useful tool specifically in emergency situations. A further need is facilitation of deaf people communication in cyberspace. Sign language gestures can be divided in two groups, including gestures represent the alphabets and those which are arbitrary signs representing specific concepts. The first group is usually introduced by the pose of hands and they are called postures while the second group usually includes motion of the hands. This paper evaluates the efficiency of kernel based feature extraction methods including kernel principle component analysis (KPCA) and kernel discriminant analysis (KDA) on Persian sign language (PSL) postures. To compare the impact of features on signs’ recognition rate, classifiers such as minimum distance (MD), support vector machine (SVM) and Neural network (NN) is used. Experimental trials indicate higher recognition rate for the kernel-based methods in comparison with those of other techniques and also previous works on PSL recognition.

Full-Text [PDF 809 kb]   (826 Downloads)    
Type of Study: Research | Subject: Information Technology

Add your comments about this article : Your username or Email: