Multi‐task learning using GNet features and SVM classifier for signature identification
Abstract Signature biometrics is a widely accepted and used modality to verify the identity of an individual in many legal and financial organisations. A writer and language‐independent signature identification method that can distinguish between the genuine and forged sample irrespective of the lan...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-IET
2021-03-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12007 |
| _version_ | 1827010020133306368 |
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| author | Anamika Jain Satish Kumar Singh Krishna Pratap Singh |
| author_facet | Anamika Jain Satish Kumar Singh Krishna Pratap Singh |
| author_sort | Anamika Jain |
| collection | DOAJ |
| description | Abstract Signature biometrics is a widely accepted and used modality to verify the identity of an individual in many legal and financial organisations. A writer and language‐independent signature identification method that can distinguish between the genuine and forged sample irrespective of the language of the signature has been proposed. To extract the distinguishing features, a pre‐trained model GoogLeNet, which is fine‐tuned with the largest signature dataset present till date (GPDS Synthetic), has been used. The proposed method is tested over the BHSig260 (contains images from two regional languages, Bengali and Hindi) dataset. With the help of the above fine‐tuned model, knowledge is transferred to the publicly available datasets – BHSig260 and MCYT‐75. The features extracted using the fine‐tuned model has been fed to the support vector machine (SVM) classifiers. With the proposed method, 96.5% and 95.7% accuracy on Bengali and Hindi datasets, and 93% on MCYT‐75 with skilled forged samples have been achieved respectively. |
| first_indexed | 2024-03-09T07:41:39Z |
| format | Article |
| id | doaj.art-d9396376ed41480c838aac11cabfd13d |
| institution | Directory Open Access Journal |
| issn | 2047-4938 2047-4946 |
| language | English |
| last_indexed | 2025-02-18T12:58:30Z |
| publishDate | 2021-03-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj.art-d9396376ed41480c838aac11cabfd13d2024-11-02T03:57:00ZengHindawi-IETIET Biometrics2047-49382047-49462021-03-0110211712610.1049/bme2.12007Multi‐task learning using GNet features and SVM classifier for signature identificationAnamika Jain0Satish Kumar Singh1Krishna Pratap Singh2Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh IndiaDepartment of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh IndiaDepartment of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh IndiaAbstract Signature biometrics is a widely accepted and used modality to verify the identity of an individual in many legal and financial organisations. A writer and language‐independent signature identification method that can distinguish between the genuine and forged sample irrespective of the language of the signature has been proposed. To extract the distinguishing features, a pre‐trained model GoogLeNet, which is fine‐tuned with the largest signature dataset present till date (GPDS Synthetic), has been used. The proposed method is tested over the BHSig260 (contains images from two regional languages, Bengali and Hindi) dataset. With the help of the above fine‐tuned model, knowledge is transferred to the publicly available datasets – BHSig260 and MCYT‐75. The features extracted using the fine‐tuned model has been fed to the support vector machine (SVM) classifiers. With the proposed method, 96.5% and 95.7% accuracy on Bengali and Hindi datasets, and 93% on MCYT‐75 with skilled forged samples have been achieved respectively.https://doi.org/10.1049/bme2.12007support vector machinesbiometrics (access control)feature extractionimage classificationhandwriting recognitionnatural language processing |
| spellingShingle | Anamika Jain Satish Kumar Singh Krishna Pratap Singh Multi‐task learning using GNet features and SVM classifier for signature identification IET Biometrics support vector machines biometrics (access control) feature extraction image classification handwriting recognition natural language processing |
| title | Multi‐task learning using GNet features and SVM classifier for signature identification |
| title_full | Multi‐task learning using GNet features and SVM classifier for signature identification |
| title_fullStr | Multi‐task learning using GNet features and SVM classifier for signature identification |
| title_full_unstemmed | Multi‐task learning using GNet features and SVM classifier for signature identification |
| title_short | Multi‐task learning using GNet features and SVM classifier for signature identification |
| title_sort | multi task learning using gnet features and svm classifier for signature identification |
| topic | support vector machines biometrics (access control) feature extraction image classification handwriting recognition natural language processing |
| url | https://doi.org/10.1049/bme2.12007 |
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