Fusing deep and hand-crafted features by deep canonically correlated contractive autoencoder for offline signature verification
Handwritten signatures are currently the most widely used and recognized form of identity authorization, which is a significant way for individuals to express their identity to information. Since the forgers learn information about the genuine signatures from the target signer in advance, there are...
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| Veröffentlicht in: | Pattern recognition Jg. 168; S. 111834 |
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01.12.2025
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| ISSN: | 0031-3203 |
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| Abstract | Handwritten signatures are currently the most widely used and recognized form of identity authorization, which is a significant way for individuals to express their identity to information. Since the forgers learn information about the genuine signatures from the target signer in advance, there are usually only minor discrepancies between skilled forged and genuine signatures. Therefore, building an automatic handwritten signature verification system to recognize skilled forgeries is a worthy challenging task. In this paper, to learn a good representation for distinguishing skilled forged and genuine signatures, we propose an offline handwritten signature verification system that fuses deep learning-based and hand-crafted features, which combines the merits of different views of features. Specifically, a novel multi-view representation learning method is proposed, named Deep Canonically Correlated Contractive Autoencoder (DCCCAE) for learning combined representations between deep and hand-crafted features. After the feature learning process, we train Support Vector Machines (SVMs) as writer-dependent classifiers for each signer to build the completed verification system. Extensive experiments and analyses on four different language datasets, such as English (CEDAR), Persian (UTSig), Bengali and Hindi (BHSig), and Chinese (SigComp2011) demonstrate that the proposed system improves the learning ability compared with the single view features and achieve the competitive performance compared with the state-of-the-art verification systems.
•A multi-view representation learning method called DCCCAE to fuse deep learning-based and hand-crafted feature.•A novel method to protect user information and privacy for offline signature verification tasks.•Extensive experiments on four public language datasets demonstrate the proposed method achieves state-of-the-art results. |
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| AbstractList | Handwritten signatures are currently the most widely used and recognized form of identity authorization, which is a significant way for individuals to express their identity to information. Since the forgers learn information about the genuine signatures from the target signer in advance, there are usually only minor discrepancies between skilled forged and genuine signatures. Therefore, building an automatic handwritten signature verification system to recognize skilled forgeries is a worthy challenging task. In this paper, to learn a good representation for distinguishing skilled forged and genuine signatures, we propose an offline handwritten signature verification system that fuses deep learning-based and hand-crafted features, which combines the merits of different views of features. Specifically, a novel multi-view representation learning method is proposed, named Deep Canonically Correlated Contractive Autoencoder (DCCCAE) for learning combined representations between deep and hand-crafted features. After the feature learning process, we train Support Vector Machines (SVMs) as writer-dependent classifiers for each signer to build the completed verification system. Extensive experiments and analyses on four different language datasets, such as English (CEDAR), Persian (UTSig), Bengali and Hindi (BHSig), and Chinese (SigComp2011) demonstrate that the proposed system improves the learning ability compared with the single view features and achieve the competitive performance compared with the state-of-the-art verification systems.
•A multi-view representation learning method called DCCCAE to fuse deep learning-based and hand-crafted feature.•A novel method to protect user information and privacy for offline signature verification tasks.•Extensive experiments on four public language datasets demonstrate the proposed method achieves state-of-the-art results. |
| ArticleNumber | 111834 |
| Author | Zhao, Xingbiao Yuan, Panli Zheng, Yuchen Zheng, Lidong |
| Author_xml | – sequence: 1 givenname: Xingbiao orcidid: 0000-0002-3973-2087 surname: Zhao fullname: Zhao, Xingbiao email: zxb@stu.shzu.edu.cn organization: College of Information Science and Technology, Shihezi University, Shihezi, 832061, China – sequence: 2 givenname: Lidong orcidid: 0009-0003-3150-2986 surname: Zheng fullname: Zheng, Lidong email: zld0608@outlook.com organization: College of Information Science and Technology, Shihezi University, Shihezi, 832061, China – sequence: 3 givenname: Panli surname: Yuan fullname: Yuan, Panli email: ypl_inf@outlook.com organization: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China – sequence: 4 givenname: Yuchen orcidid: 0000-0003-3093-6929 surname: Zheng fullname: Zheng, Yuchen email: ouczyc@outlook.com organization: College of Information Science and Technology, Shihezi University, Shihezi, 832061, China |
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| Cites_doi | 10.1109/TMM.2021.3056217 10.1109/TIFS.2019.2949425 10.1016/j.eswa.2021.116136 10.1109/ICFHR.2014.109 10.1016/j.patcog.2016.01.009 10.1109/TPAMI.2005.125 10.1023/B:VISI.0000029664.99615.94 10.1109/PRIA.2019.8785979 10.1109/TGRS.2020.3046757 10.1007/s11042-020-08728-6 10.1109/ICDAR.2011.294 10.1109/WACV48630.2021.00360 10.1007/s10032-019-00331-2 10.1142/S0218001404003630 10.1109/TIFS.2019.2924195 10.1016/j.neucom.2019.09.041 10.1016/j.neucom.2022.01.005 10.1109/TPAMI.2009.77 10.1109/CVPRW.2018.00084 10.1016/j.patcog.2013.06.026 10.1007/s10032-018-0301-6 10.1109/TSMC.1979.4310076 10.1007/s11042-019-08022-0 10.1016/j.eswa.2020.114417 10.1016/j.knosys.2021.107531 10.1016/j.patcog.2017.05.012 10.1007/s00521-023-09192-7 10.1016/j.patcog.2021.108008 10.1109/ICCV48922.2021.01156 10.1007/s00521-019-04220-x 10.1109/DAS.2016.48 10.1016/j.patrec.2018.01.021 10.1109/TGRS.2019.2952758 10.1061/(ASCE)CP.1943-5487.0000945 10.1016/j.eswa.2021.115649 10.1049/iet-bmt.2015.0058 10.1145/358790.358797 10.1016/j.eswa.2021.114602 10.1016/j.patrec.2019.06.024 10.1007/978-3-540-85920-8_3 10.1016/j.neucom.2022.08.017 10.1016/j.patcog.2018.02.027 10.1109/SAI.2014.6918213 10.1049/bme2.12007 10.1016/j.patcog.2017.05.025 10.1016/j.patcog.2021.108009 |
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| Keywords | Multi-view Hand-crafted feature Handwritten signature verification Deep feature Identity authorization |
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