dc.contributor.author | Aida Nurfarhana, Saidin | |
dc.date.accessioned | 2016-05-30T05:17:34Z | |
dc.date.available | 2016-05-30T05:17:34Z | |
dc.date.issued | 2015-06 | |
dc.identifier.uri | http://dspace.unimap.edu.my:80/xmlui/handle/123456789/41747 | |
dc.description | Access is limited to UniMAP community. | en_US |
dc.description.abstract | Signature is an important biometric of a person that is attribute of a human being which
can be used to authenticate human identity. There are lots of biometric techniques that
had been represented in the past. A biometric‟s signature verification type had play a
very important role in financial, legal transaction and commercial, truly method of
authenticating becomes more and more crucial. There are various approaches to
signature recognition with a lot of scope of researches. In this project, off- line signature
recognition and verification using support vector machine (SVM) is proposed. First,
feature vectors are extracted for a reference set of signatures using radon transform.
Then, the SVM is trained using a set of genuine and a set of forgery signature. Once
trained, the SVM is expected to recognize using an input signature. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Universiti Malaysia Perlis (UniMAP) | en_US |
dc.subject | Signature | en_US |
dc.subject | Biometric techniques | en_US |
dc.subject | Signature recognition system | en_US |
dc.subject | Signature verification | en_US |
dc.subject | Support vector machine (SVM) | en_US |
dc.title | Signature verification and recognition by using support vector machine | en_US |
dc.type | Learning Object | en_US |
dc.contributor.advisor | Dr. Md Mijanur Rahman | en_US |
dc.publisher.department | School of Computer and Communication Engineering | en_US |