Open Access

ARTICLE

An Automatic Deep Neural Network Model for Fingerprint Classification

by Amira Tarek Mahmoud^1,*, Wael A. Awad², Gamal Behery², Mohamed Abouhawwash^3,4, Mehedi Masud⁵, Hanan Aljuaid⁶, Ahmed Ismail Ebada⁷

1 Department of Computer Science, Faculty of Science, Port Said University, Port Said, Egypt
2 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, Egypt
3 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, 35516, Egypt
4 Department of Computational Mathematics, Science, and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI 48824, USA
5 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia
6 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), P. O. Box 84428, Riyadh, 11671, Saudi Arabia
7 Department of Information Systems, Faculty of Computers and Artificial Intelligence, Damietta University, New Damietta, Egypt

* Corresponding Author: Amira Tarek Mahmoud. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 2007-2023. https://doi.org/10.32604/iasc.2023.031692

Received 25 April 2022; Accepted 24 June 2022; Issue published 05 January 2023

Abstract

The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving 99% accuracy then it stops and outputs the result. Moreover, this paper proposes an end-to-end methodology for recognizing a person’s identity from the input fingerprint image based on a residual convolutional neural network. It is a complete system and is fully automated whether in the features extraction stage or the classification stage. Our goal is to automate this fingerprint recognition system because the more automatic the system is, the more time and effort it saves. Our model also allows users to react by inputting the initial values of these parameters. Then, the model updates itself until it finds the optimal values for the parameters and achieves the best accuracy. Another advantage of our algorithm is that it can recognize people from their thumb and other fingers and its ability to recognize distorted samples. Our algorithm achieved 99.75% accuracy on the public fingerprint dataset (SOCOFing). This is the best accuracy compared with other models.

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Keywords

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