A simple sign language recognition system using affine moment blur invariant features
International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia.
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Universiti Malaysia Perlis (UniMAP)
2012
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my.unimap-216202012-11-05T08:58:01Z A simple sign language recognition system using affine moment blur invariant features Rajkumar, Palaniappan Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shuhanaz, Zanar Azalan prkmect@gmail.com Sign language recognition Hand gesture Affine Moment Blur invariants International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia. Sign language recognition is one of the most promising sub-fields in gesture recognition research. Effective sign language recognition would grant the deaf and hard-of-hearing expanded tools for communicating with both other people and machines. Hand gesture is one of the typical methods used in sign language for non-verbal communication. It is most commonly used by people who have hearing or speech problems to communicate among themselves or with normal people. Developing a sign language recognition system will help the hearing impaired to communicate more fluently with the normal people. This paper presents a simple sign language recognition system that has been developed using skin color segmentation and Artificial Neural Network. The Affine Moment Blur invariants extracted from the right and left hand gesture images are used as feature vector to develop a network model. The system has been implemented and tested for its validity. Experimental results show that the recognition rate is 97.19%. 2012-11-05T08:58:01Z 2012-11-05T08:58:01Z 2010-10-16 Working Paper 978-967-5760-03-7 http://hdl.handle.net/123456789/21620 en Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010) Universiti Malaysia Perlis (UniMAP) Centre for Graduate Studies |
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Malaysia |
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Universiti Malaysia Perlis |
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| language |
English |
| topic |
Sign language recognition Hand gesture Affine Moment Blur invariants |
| spellingShingle |
Sign language recognition Hand gesture Affine Moment Blur invariants Rajkumar, Palaniappan Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shuhanaz, Zanar Azalan A simple sign language recognition system using affine moment blur invariant features |
| description |
International Postgraduate Conference On Engineering (IPCE 2010), 16th - 17th October 2010 organized by Centre for Graduate Studies, Universiti Malaysia Perlis (UniMAP) at School of Mechatronic Engineering, Pauh Putra Campus, Perlis, Malaysia. |
| author2 |
prkmect@gmail.com |
| author_facet |
prkmect@gmail.com Rajkumar, Palaniappan Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shuhanaz, Zanar Azalan |
| format |
Working Paper |
| author |
Rajkumar, Palaniappan Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shuhanaz, Zanar Azalan |
| author_sort |
Rajkumar, Palaniappan |
| title |
A simple sign language recognition system using affine moment blur invariant features |
| title_short |
A simple sign language recognition system using affine moment blur invariant features |
| title_full |
A simple sign language recognition system using affine moment blur invariant features |
| title_fullStr |
A simple sign language recognition system using affine moment blur invariant features |
| title_full_unstemmed |
A simple sign language recognition system using affine moment blur invariant features |
| title_sort |
simple sign language recognition system using affine moment blur invariant features |
| publisher |
Universiti Malaysia Perlis (UniMAP) |
| publishDate |
2012 |
| url |
http://dspace.unimap.edu.my/xmlui/handle/123456789/21620 |
| _version_ |
1643793415626817536 |