<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also contain dynamic information. This information is acquired using a digital input device, such as a graphic tablet or a digital screen and stylus. Information about the dynamics of the signing process is stored in the form of signals that change over time, including pen velocity, pressure, and more. These dynamics are characteristic of an individual and are difficult for a human to forge. However, it is an interesting research issue whether the values of signals describing a dynamic signature can be predicted using artificial intelligence methods. Predicting the dynamics of the signals describing a signature would benefit various scientific problems, including improving the quality of reference signals by detecting anomalies, creating signature templates better suited to individuals, and more effectively detecting potential forgeries by identity verification systems. In this paper, we propose a method for predicting dynamic signature signals using an artificial neural network. The method was evaluated using samples collected in the DeepSignDB database, distributed by BiDA Lab.</p>
</abstract>

A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also contain dynamic information. This information is acquired using a digital input device, such as a graphic tablet or a digital screen and stylus. Information about the dynamics of the signing process is stored in the form of signals that change over time, including pen velocity, pressure, and more. These dynamics are characteristic of an individual and are difficult for a human to forge. However, it is an interesting research issue whether the values of signals describing a dynamic signature can be predicted using artificial intelligence methods. Predicting the dynamics of the signals describing a signature would benefit various scientific problems, including improving the quality of reference signals by detecting anomalies, creating signature templates better suited to individuals, and more effectively detecting potential forgeries by identity verification systems. In this paper, we propose a method for predicting dynamic signature signals using an artificial neural network. The method was evaluated using samples collected in the DeepSignDB database, distributed by BiDA Lab.

Evaluating Neural Network Models For Predicting Dynamic Signature Signals

Department of Intelligent Computer Systems

Nanjing University of Posts and Telecommunications

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Evaluating Neural Network Models For Predicting Dynamic Signature Signals"}

A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also...

Evaluating Neural Network Models For Predicting Dynamic Signature Signals

Online veröffentlicht: 29. Juli 2024

Seitenbereich: 361 - 372

Eingereicht: 16. Jan. 2024

Akzeptiert: 16. Juli 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0019

Schlüsselwörter
dynamic signature, prediction, neural networks, artificial intelligence, bio-metrics

© 2024 Marcin Zalasiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Evaluating Neural Network Models For Predicting Dynamic Signature Signals

Online veröffentlicht: 29. Juli 2024

Seitenbereich: 361 - 372

Eingereicht: 16. Jan. 2024

Akzeptiert: 16. Juli 2024

DOI: https://doi.org/10.2478/jaiscr-2024-0019

Schlüsselwörterdynamic signature, prediction, neural networks, artificial intelligence, bio-metrics

© 2024 Marcin Zalasiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
dynamic signature, prediction, neural networks, artificial intelligence, bio-metrics