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R-SigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification.
- Source :
-
Pattern Recognition Letters . Oct2021, Vol. 150, p189-196. 8p. - Publication Year :
- 2021
-
Abstract
- • The design of a reduced feature space CNN applied to the signature verification task. • The introduction of a relaxed loss to better exploit the designed network. • The performance advancement with respect to current literature works. Handwritten signatures are a widespread biometric trait for person identification and verification. Reliable authentication and authorization of individuals are, however, challenging tasks due to possible skilled forgeries; especially when a forger has access to a given signature and deliberately tries to imitate it. This problem is even more emphasised in offline signature verification, where dynamic signature information is lost, resulting, as a consequence, in an increased difficulty discerning between genuine and forged signatures. To address this issue, solutions based on convolutional neural networks (CNN) are currently being devised to automatically extract features from a signature. Although highly performing, these methods require a high number of learnable parameters to produce meaningful signature representations, ultimately leading to long training times. In this paper, the R-SigNet architecture, a multi-task approach exploiting a relaxed loss to learn a reduced feature space for writer-independent (WI) signature verification, is presented. Compact generic features are automatically extracted by this network, so that a support vector machine (SVM) can be trained and tested in offline writer-dependent (WD) mode. By leveraging a small generic feature space, the proposed system achieves improved performances and reduced training times with respect to the current literature, as shown by the results obtained on several benchmark datasets. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 150
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 152272262
- Full Text :
- https://doi.org/10.1016/j.patrec.2021.06.033