R-SigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification

Autor: Manoochehr Joodi Bigdello, Marco Raoul Marini, Alessio Fagioli, Luigi Cinque, Danilo Avola
Rok vydání: 2021
Předmět:
Zdroj: Pattern Recognition Letters. 150:189-196
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2021.06.033
Popis: 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.
Databáze: OpenAIRE
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