A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification
Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional S...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/6/1216 |
| _version_ | 1797482009538330624 |
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| author | Wanghui Xiao Yuting Ding |
| author_facet | Wanghui Xiao Yuting Ding |
| author_sort | Wanghui Xiao |
| collection | DOAJ |
| description | Offline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification. |
| first_indexed | 2024-03-09T22:22:09Z |
| format | Article |
| id | doaj.art-6a3761579fd64779b90ba557f7f06cdb |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:22:09Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-6a3761579fd64779b90ba557f7f06cdb2023-11-23T19:12:42ZengMDPI AGSymmetry2073-89942022-06-01146121610.3390/sym14061216A Two-Stage Siamese Network Model for Offline Handwritten Signature VerificationWanghui Xiao0Yuting Ding1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaSchool of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaOffline handwritten signature verification is one of the most prevalent and prominent biometric methods in many application fields. Siamese neural network, which can extract and compare the writers’ style features, proves to be efficient in verifying the offline signature. However, the traditional Siamese neural network fails to represent the writers’ writing style fully and suffers from low performance when the distribution of positive and negative handwritten signature samples is unbalanced. To address this issue, this study proposes a two-stage Siamese neural network model for accurate offline handwritten signature verification with two main ideas: (a) adopting a two-stage Siamese neural network to verify original and enhanced handwritten signatures simultaneously, and (b) utilizing the Focal Loss to deal with the extreme imbalance between positive and negative offline signatures. Experimental results on four challenging handwritten signature datasets with different languages demonstrate that compared with state-of-the-art models, our proposed model achieves better performance. Furthermore, this study tries to extend the proposed model to the Chinese signature dataset in the real environment, which is a significant attempt in the field of Chinese signature identification.https://www.mdpi.com/2073-8994/14/6/1216Siamese neural networkoffline handwritten signature verificationdata enhancementFocal loss |
| spellingShingle | Wanghui Xiao Yuting Ding A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification Symmetry Siamese neural network offline handwritten signature verification data enhancement Focal loss |
| title | A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification |
| title_full | A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification |
| title_fullStr | A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification |
| title_full_unstemmed | A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification |
| title_short | A Two-Stage Siamese Network Model for Offline Handwritten Signature Verification |
| title_sort | two stage siamese network model for offline handwritten signature verification |
| topic | Siamese neural network offline handwritten signature verification data enhancement Focal loss |
| url | https://www.mdpi.com/2073-8994/14/6/1216 |
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