1. SISN
  2. All Volumes and Issues
  3. Volume 15, 2024
  4. Fingerprint Identification Method Based on Convulsional Neural Networks

Fingerprint Identification Method Based on Convulsional Neural Networks

SISN.
2024;
Volume 15
: pp. 1 - 14
https://doi.org/10.23939/sisn2024.15.001
Authors:
  1. Yurii Myshkovskyi
  2. Mariia Nazarkevych
1
Lviv Polytechnic National University, Information Systems and Networks Department
2
Lviv Polytechnic National University, Lviv, Ukraine

The article presents an advanced method of fingerprint identification based on convolutional neural network (CNN) technology. This work elaborately describes the development and implementation process of a specialized CNN architecture for detecting and verifying the authenticity of fingerprints. Utilizing the comprehensive Socofing dataset allowed for an in -depth analysis of the model’s ability to distinguish between genuine and fabricated fingerprints, where the model demonstrated impressive accuracy – up to 98.964%. Special attention is given to error analysis, including the false discovery and omission rates, pointing towards potential directions for further improvement. Besides highlighting the technical aspects and high identification accuracy, the article also addresses potential challenges and limitations that the method might encounter. This includes issues related to the imbalance and diversity of data in the Socofing set, as well as limitations associated with computational resources when training deep neural networks. Potential pathways for model optimization are discussed, particularly focusing on reducing the false omission rate, which could improve user experience in authentication. The concluding section of the article emphasizes the importance of the presented work for the security sector, where precise authentication of fingerprint images is critically needed. The obtained results can be considered a solid foundation for future scientific developments in this direction. Additionally, the need for systematic updates and modifications of the model is highlighted to adapt it to continually improved imitation techniques, ensuring its long-term relevance and effectiveness.

fingerprints
convolutional neural networks (CNN)
biometric authentication
forgeries
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