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Development of an Improved Fingerprint Biometric Model for Automated Teller Machine Security System
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity, Machine Learning
DOI: 10.64388/IREV10I4-1723629
Abstract
Fingerprint authentication provides a biometric approach for improving user verification in ATM systems. However, fingerprint images may contain variations and alterations that can affect reliable classification. This study developed a Hybrid Autoencoder–CNN–ANN model for fingerprint-based ATM authentication using the Sokoto Coventry Fingerprint Dataset (SOCOFing). The model used an autoencoder for feature representation, convolutional neural network (CNN) layers for spatial feature extraction, and artificial neural network (ANN) layers for classification. Fingerprint images were converted to grayscale, resized to 128 × 128 pixels, and normalized before training. The autoencoder was first trained to learn compact fingerprint representations, after which the learned features were used by the CNN–ANN classification stage. Experimental results showed that the developed model achieved a test accuracy of 89.48%, with 95% recall and 82% F1-score for the real fingerprint class, while the weighted F1-score was 90%. The confusion matrix recorded 3,934 altered fingerprints correctly classified as altered and 1,435 real fingerprints correctly classified as real. However, 563 altered fingerprints were classified as real, indicating that false acceptance remains a limitation. Compared with the CNN-based study of Okorie et al. [1], the developed model achieved higher accuracy, recall, and F1-score, although its precision was lower. The findings demonstrate the feasibility of combining autoencoder, CNN, and ANN techniques for fingerprint classification in a simulated ATM authentication environment.
Keywords
Fingerprint authentication; biometric security; autoencoder; convolutional neural network; artificial neural network; SOCOFing; ATM security; deep learning.
How to cite this paper
@article{1723629,
author = {Samuel, O. D., Prof. Afolabi, A. O., Prof. Adeosun O. O., Akande, O. V., Adepoju, J. A.},
title = {Development of an Improved Fingerprint Biometric Model for Automated Teller Machine Security System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {249-259},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723629.pdf},
abstract = {Fingerprint authentication provides a biometric approach for improving user verification in ATM systems. However, fingerprint images may contain variations and alterations that can affect reliable classification. This study developed a Hybrid Autoencoder–CNN–ANN model for fingerprint-based ATM authentication using the Sokoto Coventry Fingerprint Dataset (SOCOFing). The model used an autoencoder for feature representation, convolutional neural network (CNN) layers for spatial feature extraction, and artificial neural network (ANN) layers for classification. Fingerprint images were converted to grayscale, resized to 128 × 128 pixels, and normalized before training. The autoencoder was first trained to learn compact fingerprint representations, after which the learned features were used by the CNN–ANN classification stage. Experimental results showed that the developed model achieved a test accuracy of 89.48%, with 95% recall and 82% F1-score for the real fingerprint class, while the weighted F1-score was 90%. The confusion matrix recorded 3,934 altered fingerprints correctly classified as altered and 1,435 real fingerprints correctly classified as real. However, 563 altered fingerprints were classified as real, indicating that false acceptance remains a limitation. Compared with the CNN-based study of Okorie et al. [1], the developed model achieved higher accuracy, recall, and F1-score, although its precision was lower. The findings demonstrate the feasibility of combining autoencoder, CNN, and ANN techniques for fingerprint classification in a simulated ATM authentication environment.},
keywords = {Fingerprint authentication; biometric security; autoencoder; convolutional neural network; artificial neural network; SOCOFing; ATM security; deep learning.},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1723629}
}