Biometric signature verification system based on freeman chain code and k-nearest neighbor
Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into th...
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2017
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Online Access: | http://eprints.utm.my/id/eprint/74867/1/AiniNajwaAzmi2017_BiometricSignatureVerificationSystembasedonFreeman.pdf http://eprints.utm.my/id/eprint/74867/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988429321&doi=10.1007%2fs11042-016-3831-2&partnerID=40&md5=2f4a3d4dfd4e1af4e657f7911e62126b |
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my.utm.748672018-03-21T00:28:18Z http://eprints.utm.my/id/eprint/74867/ Biometric signature verification system based on freeman chain code and k-nearest neighbor Azmi, A. N. Nasien, D. Omar, F. S. QA75 Electronic computers. Computer science Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is 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. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. 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 against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database. Springer New York LLC 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/74867/1/AiniNajwaAzmi2017_BiometricSignatureVerificationSystembasedonFreeman.pdf Azmi, A. N. and Nasien, D. and Omar, F. S. (2017) Biometric signature verification system based on freeman chain code and k-nearest neighbor. Multimedia Tools and Applications, 76 (14). pp. 15341-15355. ISSN 1380-7501 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988429321&doi=10.1007%2fs11042-016-3831-2&partnerID=40&md5=2f4a3d4dfd4e1af4e657f7911e62126b DOI:10.1007/s11042-016-3831-2 |
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QA75 Electronic computers. Computer science Azmi, A. N. Nasien, D. Omar, F. S. Biometric signature verification system based on freeman chain code and k-nearest neighbor |
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Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is 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. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. 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 against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database. |
format |
Article |
author |
Azmi, A. N. Nasien, D. Omar, F. S. |
author_facet |
Azmi, A. N. Nasien, D. Omar, F. S. |
author_sort |
Azmi, A. N. |
title |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
title_short |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
title_full |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
title_fullStr |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
title_full_unstemmed |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
title_sort |
biometric signature verification system based on freeman chain code and k-nearest neighbor |
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Springer New York LLC |
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2017 |
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http://eprints.utm.my/id/eprint/74867/1/AiniNajwaAzmi2017_BiometricSignatureVerificationSystembasedonFreeman.pdf http://eprints.utm.my/id/eprint/74867/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988429321&doi=10.1007%2fs11042-016-3831-2&partnerID=40&md5=2f4a3d4dfd4e1af4e657f7911e62126b |
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