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Article

Online Signature Verification Based on a Single Template via Elastic Curve Matching

1
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2
Hubei Collaborative Innovation Center for High-Efficient Utilization of Solar Energy, Hubei University of Technology, Wuhan 430068, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4858; https://doi.org/10.3390/s19224858
Received: 29 August 2019 / Revised: 27 October 2019 / Accepted: 29 October 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Biometric Systems)
Person verification using online handwritten signatures is one of the most widely researched behavior-biometrics. Many signature verification systems typically require five, ten, or even more signatures for an enrolled user to provide an accurate verification of the claimed identity. To mitigate this drawback, this paper proposes a new elastic curve matching using only one reference signature, which we have named the curve similarity model (CSM). In the CSM, we give a new definition of curve similarity and its calculation method. We use evolutionary computation (EC) to search for the optimal matching between two curves under different similarity transformations, so as to obtain the similarity distance between two curves. Referring to the geometric similarity property, curve similarity can realize translation, stretching and rotation transformation between curves, thus adapting to the inconsistency of signature size, position and rotation angle in signature curves. In the matching process of signature curves, we design a sectional optimal matching algorithm. On this basis, for each section, we develop a new consistent and discriminative fusion feature extraction for identifying the similarity of signature curves. The experimental results show that our system achieves the same performance with five samples assessed with multiple state-of-the-art automatic signature verifiers and multiple datasets. Furthermore, it suggests that our system, with a single reference signature, is capable of achieving a similar performance to other systems with up to five signatures trained. View Full-Text
Keywords: curve similarity; curve similarity model; curve similarity transformation; similarity distance; segmentation matching; evolutionary computation curve similarity; curve similarity model; curve similarity transformation; similarity distance; segmentation matching; evolutionary computation
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MDPI and ACS Style

Hu, H.; Zheng, J.; Zhan, E.; Tang, J. Online Signature Verification Based on a Single Template via Elastic Curve Matching. Sensors 2019, 19, 4858. https://doi.org/10.3390/s19224858

AMA Style

Hu H, Zheng J, Zhan E, Tang J. Online Signature Verification Based on a Single Template via Elastic Curve Matching. Sensors. 2019; 19(22):4858. https://doi.org/10.3390/s19224858

Chicago/Turabian Style

Hu, Huacheng, Jianbin Zheng, Enqi Zhan, and Jing Tang. 2019. "Online Signature Verification Based on a Single Template via Elastic Curve Matching" Sensors 19, no. 22: 4858. https://doi.org/10.3390/s19224858

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