Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition
Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Maragheh
2020-07-01
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| Series: | Sahand Communications in Mathematical Analysis |
| Subjects: | |
| Online Access: | https://scma.maragheh.ac.ir/article_38395_26252c7c74e40fc0b3676cf37cdf20eb.pdf |
| _version_ | 1818406367897059328 |
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| author | Atefeh Foroozandeh Ataollah Askari Hemmat Hossein Rabbani |
| author_facet | Atefeh Foroozandeh Ataollah Askari Hemmat Hossein Rabbani |
| author_sort | Atefeh Foroozandeh |
| collection | DOAJ |
| description | Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten signatures are composed of curves and the performance of a signature verification/recognition system is directly related to the edge structures, subbands of shearlet transform of signature images are good candidates for input information to the system. Furthermore, by using transfer learning of some pre-trained models, appropriate features would be extracted. In this study, four pre-trained models have been used: SigNet and SigNet-F (trained on offline signature datasets), VGG16 and VGG19 (trained on ImageNet dataset). Experiments have been conducted using three datasets: UTSig, FUM-PHSD and MCYT-75. Obtained experimental results, in comparison with the literature, verify the effectiveness of the presented method in both signature verification and signature recognition. |
| first_indexed | 2024-12-14T09:10:49Z |
| format | Article |
| id | doaj.art-9d747c2a746a4f3cb4d7d486aa92c990 |
| institution | Directory Open Access Journal |
| issn | 2322-5807 2423-3900 |
| language | English |
| last_indexed | 2024-12-14T09:10:49Z |
| publishDate | 2020-07-01 |
| publisher | University of Maragheh |
| record_format | Article |
| series | Sahand Communications in Mathematical Analysis |
| spelling | doaj.art-9d747c2a746a4f3cb4d7d486aa92c9902022-12-21T23:08:34ZengUniversity of MaraghehSahand Communications in Mathematical Analysis2322-58072423-39002020-07-0117313110.22130/scma.2019.99098.53638395Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and RecognitionAtefeh Foroozandeh0Ataollah Askari Hemmat1Hossein Rabbani2Department of Applied Mathematics, Faculty of Sciences and Modern Technology, Graduate University of Advanced Technology, Kerman, Iran.Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.Despite the growing growth of technology, handwritten signature has been selected as the first option between biometrics by users. In this paper, a new methodology for offline handwritten signature verification and recognition based on the Shearlet transform and transfer learning is proposed. Since, a large percentage of handwritten signatures are composed of curves and the performance of a signature verification/recognition system is directly related to the edge structures, subbands of shearlet transform of signature images are good candidates for input information to the system. Furthermore, by using transfer learning of some pre-trained models, appropriate features would be extracted. In this study, four pre-trained models have been used: SigNet and SigNet-F (trained on offline signature datasets), VGG16 and VGG19 (trained on ImageNet dataset). Experiments have been conducted using three datasets: UTSig, FUM-PHSD and MCYT-75. Obtained experimental results, in comparison with the literature, verify the effectiveness of the presented method in both signature verification and signature recognition.https://scma.maragheh.ac.ir/article_38395_26252c7c74e40fc0b3676cf37cdf20eb.pdfoffline handwritten signaturesignature verificationsignature recognitionshearlet transformtransfer learning |
| spellingShingle | Atefeh Foroozandeh Ataollah Askari Hemmat Hossein Rabbani Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition Sahand Communications in Mathematical Analysis offline handwritten signature signature verification signature recognition shearlet transform transfer learning |
| title | Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition |
| title_full | Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition |
| title_fullStr | Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition |
| title_full_unstemmed | Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition |
| title_short | Use of the Shearlet Transform and Transfer Learning in Offline Handwritten Signature Verification and Recognition |
| title_sort | use of the shearlet transform and transfer learning in offline handwritten signature verification and recognition |
| topic | offline handwritten signature signature verification signature recognition shearlet transform transfer learning |
| url | https://scma.maragheh.ac.ir/article_38395_26252c7c74e40fc0b3676cf37cdf20eb.pdf |
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