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International Journal of Information and Communication Technology Research (IJICT، جلد ۴، شماره ۱، صفحات ۲۱-۲۸
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Static Persian Sign Language Recognition Using Kernel-Based Feature Extraction
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله | Milad Moghaddam


| Manoochehr Nahvi


| Reza P.R. Hasanzadeh


نشانی اینترنتی http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-165&slc_lang=fa&sid=1
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/417/article-417-1212468.pdf
کد مقاله (doi)
زبان مقاله منتشر شده fa
موضوعات مقاله منتشر شده فناوری اطلاعات
نوع مقاله منتشر شده پژوهشی
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