Freeman chain code as representation in offline signature verification system
Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fi...
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2016
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| Online Access: | http://eprints.utm.my/71449/1/AiniNajwaAzmi2016_Freemanchaincodeasrepresentation.pdf |
| _version_ | 1796861772252053504 |
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| author | Azmi, Aini Najwa Nasien, Dewi Abu Samah, Azurah |
| author_facet | Azmi, Aini Najwa Nasien, Dewi Abu Samah, Azurah |
| author_sort | Azmi, Aini Najwa |
| collection | ePrints |
| description | Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features. |
| first_indexed | 2024-03-05T20:01:28Z |
| format | Article |
| id | utm.eprints-71449 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:01:28Z |
| publishDate | 2016 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-714492017-11-22T12:07:32Z http://eprints.utm.my/71449/ Freeman chain code as representation in offline signature verification system Azmi, Aini Najwa Nasien, Dewi Abu Samah, Azurah QA75 Electronic computers. Computer science Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/71449/1/AiniNajwaAzmi2016_Freemanchaincodeasrepresentation.pdf Azmi, Aini Najwa and Nasien, Dewi and Abu Samah, Azurah (2016) Freeman chain code as representation in offline signature verification system. Jurnal Teknologi, 78 (8-2). pp. 89-94. ISSN 0127-9696 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988446534&doi=10.11113%2fjt.v78.9546&partnerID=40&md5=e1583232d542b517a9946b8fb961b8b4 |
| spellingShingle | QA75 Electronic computers. Computer science Azmi, Aini Najwa Nasien, Dewi Abu Samah, Azurah Freeman chain code as representation in offline signature verification system |
| title | Freeman chain code as representation in offline signature verification system |
| title_full | Freeman chain code as representation in offline signature verification system |
| title_fullStr | Freeman chain code as representation in offline signature verification system |
| title_full_unstemmed | Freeman chain code as representation in offline signature verification system |
| title_short | Freeman chain code as representation in offline signature verification system |
| title_sort | freeman chain code as representation in offline signature verification system |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/71449/1/AiniNajwaAzmi2016_Freemanchaincodeasrepresentation.pdf |
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