Biometric signature verification system based on freeman chain code and k-nearest neighbor
Abstract 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 decompose...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Azmi, Aini Najwa [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 76(2016), 14 vom: 19. Sept., Seite 15341-15355 |
|---|---|
| Übergeordnetes Werk: |
volume:76 ; year:2016 ; number:14 ; day:19 ; month:09 ; pages:15341-15355 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-016-3831-2 |
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OLC203503633X |
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| 520 | |a Abstract 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. | ||
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| 700 | 1 | |a Omar, Fakhrul Syakirin |4 aut | |
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10.1007/s11042-016-3831-2 doi (DE-627)OLC203503633X (DE-He213)s11042-016-3831-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Azmi, Aini Najwa verfasserin aut Biometric signature verification system based on freeman chain code and k-nearest neighbor 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Offline signature verification system Preprocessing Feature extraction Freeman chain code Euclidean distance Nasien, Dewi aut Omar, Fakhrul Syakirin aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 14 vom: 19. Sept., Seite 15341-15355 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:14 day:19 month:09 pages:15341-15355 https://doi.org/10.1007/s11042-016-3831-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 14 19 09 15341-15355 |
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10.1007/s11042-016-3831-2 doi (DE-627)OLC203503633X (DE-He213)s11042-016-3831-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Azmi, Aini Najwa verfasserin aut Biometric signature verification system based on freeman chain code and k-nearest neighbor 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Offline signature verification system Preprocessing Feature extraction Freeman chain code Euclidean distance Nasien, Dewi aut Omar, Fakhrul Syakirin aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 14 vom: 19. Sept., Seite 15341-15355 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:14 day:19 month:09 pages:15341-15355 https://doi.org/10.1007/s11042-016-3831-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 14 19 09 15341-15355 |
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10.1007/s11042-016-3831-2 doi (DE-627)OLC203503633X (DE-He213)s11042-016-3831-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Azmi, Aini Najwa verfasserin aut Biometric signature verification system based on freeman chain code and k-nearest neighbor 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Offline signature verification system Preprocessing Feature extraction Freeman chain code Euclidean distance Nasien, Dewi aut Omar, Fakhrul Syakirin aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 14 vom: 19. Sept., Seite 15341-15355 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:14 day:19 month:09 pages:15341-15355 https://doi.org/10.1007/s11042-016-3831-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 14 19 09 15341-15355 |
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10.1007/s11042-016-3831-2 doi (DE-627)OLC203503633X (DE-He213)s11042-016-3831-2-p DE-627 ger DE-627 rakwb eng 070 004 VZ Azmi, Aini Najwa verfasserin aut Biometric signature verification system based on freeman chain code and k-nearest neighbor 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2016 Abstract 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. Offline signature verification system Preprocessing Feature extraction Freeman chain code Euclidean distance Nasien, Dewi aut Omar, Fakhrul Syakirin aut Enthalten in Multimedia tools and applications Springer US, 1995 76(2016), 14 vom: 19. Sept., Seite 15341-15355 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:76 year:2016 number:14 day:19 month:09 pages:15341-15355 https://doi.org/10.1007/s11042-016-3831-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 76 2016 14 19 09 15341-15355 |
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Biometric signature verification system based on freeman chain code and k-nearest neighbor |
| abstract |
Abstract 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. © The Author(s) 2016 |
| abstractGer |
Abstract 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. © The Author(s) 2016 |
| abstract_unstemmed |
Abstract 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. © The Author(s) 2016 |
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| title_short |
Biometric signature verification system based on freeman chain code and k-nearest neighbor |
| url |
https://doi.org/10.1007/s11042-016-3831-2 |
| remote_bool |
false |
| author2 |
Nasien, Dewi Omar, Fakhrul Syakirin |
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Nasien, Dewi Omar, Fakhrul Syakirin |
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| doi_str |
10.1007/s11042-016-3831-2 |
| up_date |
2024-07-03T23:32:22.889Z |
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7.402916 |