A Performance Trade-Off Analysis Between Minutiae-Based Algorithm and Convolutional Neural Networks in Fingerprint Image Identification

Raden Bagus Bambang  Sumantri; Fajar Mahardika; Dede Yusuf; Tri Stiyo  Famuji

doi:10.52436/1.jutif.2026.7.4.5609

Authors

Raden Bagus Bambang Sumantri Informatics, Faculty of Pharmacy, Science and Technology, Al-Irsyad University of Cilacap, Indonesia
Fajar Mahardika Informatics Engineering, Department of Computer and Business, Cilacap State Polytechnic, Indonesia
Dede Yusuf Informatics, Faculty of Pharmacy, Science and Technology, Al-Irsyad University of Cilacap, Indonesia
Tri Stiyo Famuji Informatics, Faculty of Pharmacy, Science and Technology, Al-Irsyad University of Cilacap, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.4.5609

Keywords:

Biometric authentication, Convolutional Neural Network (CNN), False Acceptance Rate (FAR), False Rejection Rate (FRR), Fingerprint identification, Minutiae-Based algorithm

Abstract

Fingerprint image identification plays a crucial role in biometric authentication systems; however, selecting an appropriate algorithm remains challenging due to trade-offs between identification accuracy and computational efficiency. This study aims to comparatively evaluate the performance of a traditional Minutiae-Based algorithm and a Convolutional Neural Network (CNN) for fingerprint image identification to determine their respective strengths and limitations. The Minutiae-Based method extracts distinctive ridge features, such as ridge endings and bifurcations, followed by a similarity-based matching process. In contrast, the CNN model automatically learns discriminative features from raw fingerprint images through deep learning. Experiments were conducted on a multi-subject fingerprint dataset, and performance was assessed using identification accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and computation time per image. The results show that CNN achieved a higher identification accuracy of 98.6%, with a FAR of 1.2% and FRR of 1.4%, outperforming the Minutiae-Based algorithm, which obtained 92.3% accuracy, 4.8% FAR, and 3.9% FRR. However, the Minutiae-Based approach demonstrated superior computational efficiency, requiring an average processing time of 0.42 seconds per image compared to 1.35 seconds for CNN. These findings highlight a clear performance trade-off between accuracy and processing speed. The novelty of this study lies in providing a structured quantitative comparison that integrates accuracy, security metrics, and computational cost within a unified evaluation framework. The results contribute to the development of biometric systems by offering practical guidance for selecting fingerprint identification algorithms based on application-specific requirements, whether prioritizing high recognition accuracy or real-time computational efficiency.

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References

A. Alshardan, A. Kumar, M. Alghamdi, and M. M., “Multimodal biometric identification: Leveraging convolutional neural network (CNN) architectures and fusion techniques with fingerprint and finger vein data,” PeerJ Computer Science, 2024, doi: 10.7717/peerj-cs.2440.

F. Mahardika and R. B. B. Sumantri, “Implementation of payment gateway in the mobile-based Pawon Mbok E eating house ordering system,” Journal of Innovation in Information Technology and Applications, pp. 60–70, 2024.

H. Muhammad and Z. Khalaf, “A survey of fingerprint identification system using deep learning,” 2025. [Online]. Available: ResearchGate preprint.

S. Singh, D. Nishad, and S. K., “Enhancing fingerprint identification using fuzzy-ANN minutiae matching,” Microprocessors and Microsystems, 2025.

D. Olayiwola et al., “Fingerprint verification and identification system using a gravitational search algorithm-optimized deep convolutional neural network,” AU Journal of Engineering and Technology, 2023.

N. Alsharman, A. Saaidah, O. Almomani, I. Jawarneh, and L. Al-Qaisi, “Pattern mathematical model for fingerprint security using bifurcation minutiae extraction and neural network feature selection,” Security and Communication Networks, vol. 2022, 2022, doi: 10.1155/2022/4375232.

M. Ali, C. Wang, and M. A., “A deep CNN-based feature extraction and matching of pores for fingerprint recognition,” IEEE Transactions on Biometrics, Behavior, and Identity Science, 2024.

R. B. B. Sumantri, G. Subari, F. Mahardika, and H. Jayusman, “Perbandingan efisiensi waktu proses pengaksesan data antara query berbentuk join dengan subselect,” METHOMIKA: Jurnal Manajemen Informatika dan Komputerisasi Akuntansi, vol. 7, no. 1, pp. 25–33, 2023, doi: 10.46880/jmika.vol7no1.pp25-33.

F. Mahardika and R. B. B. Sumantri, “Penerapan metode fuzzy logic pada sistem pengaturan kecepatan mesin produksi,” 2022.

A. Chowdhury and M. Islam, “Contactless fingerprint recognition using deep learning—A systematic review,” Journal of Cybersecurity and Privacy, vol. 2, no. 3, 2022.

R. Singh, R. Singh, R. K. Tripathi, and P. Agarwal, “Fingerprint recognition using artificial neural networks,” CSI Transactions on ICT, vol. 95, no. 2, pp. 127–135, Jun. 2025, doi: 10.1007/S40010-025-00917-Y.

C. Militello, L. Rundo, S. Vitabile, and V. C., “Fingerprint classification based on deep learning approaches: Experimental findings and comparisons,” Symmetry, vol. 13, no. 5, p. 750, 2021.

L. Efrizoni, S. Armoogum, and M. Z., “Deep learning innovations in fingerprint recognition: A comparative study of model efficiencies,” International Journal of AI and Machine Learning, vol. 1, no. 1, 2024, doi: 10.58723/ijaaiml.v1i1.294.

A. Alotaibi, M. Hussain, and H. A., “Cross-sensor fingerprint recognition using convolutional neural network and canonical correlation analysis,” IEEE Access, 2024.

S. Barzut, M. Milosavljević, S. Adamović, and M. S., “A novel fingerprint biometric cryptosystem based on convolutional neural networks,” Mathematics, vol. 9, no. 7, p. 730, 2021.

K. Dushku, “Fingerprint recognition system using minutiae-based extraction and machine learning algorithms,” 2024. [Online]. Available: ResearchGate preprint.

P. Görgel and A. E., “Minutiae-based fingerprint identification using Gabor wavelets and CNN architecture,” Electrica, 2021.

F. Mahardika, A. R. Naufal, and M. Al Amin, “Desain UI dan UX dalam sistem informasi akademik menggunakan metode extreme programming,” Progresif: Jurnal Ilmiah Komputer, vol. 19, no. 1, pp. 105–116, 2023, doi: 10.35889/PROGRESIF.V19I1.1023.

W. Jian, Y. Zhou, and H. L., “Lightweight convolutional neural network based on singularity ROI for fingerprint classification,” IEEE Access, vol. 8, pp. 54593–54604, 2020.

M. Chhabra et al., “Improving automated latent fingerprint detection and segmentation using deep convolutional neural network,” Neural Computing and Applications, vol. 35, no. 9, pp. 6471–6497, 2023.

M. B. Bhilavade et al., “Fingerprint reconstruction: Convolutional neural network based approach to improve fingerprint recognition,” in Proc. IEEE Int. Workshop, 2024.

A. Chowdhury, S. Kirchgasser, A. Uhl, and A. Ross, “Can a CNN automatically learn the significance of minutiae points for fingerprint matching?,” in Proc. IEEE Winter Conf. Applications of Computer Vision (WACV), 2020, pp. 1–10.

T. Elbendary and H. M., “Palmprint authentication techniques: A comparative study,” in Proc. ITC-Egypt, 2024.

B. Zhou et al., “Fast minutiae extractor using neural network,” Pattern Recognition, vol. 103, 2020.

R. Kuzu et al., “On-the-fly finger-vein-based biometric recognition using deep neural networks,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 345–356, 2020.

N. Martins, J. Silva, and A. B., “Fingerprint recognition in forensic scenarios,” Sensors, vol. 24, no. 2, p. 664, 2024.

S. F. Z. Sajjad et al., “Enhanced fingerprint matching using convolutional neural network and multi-head self-attention,” Journal of Intelligent & Fuzzy Systems, vol. 28, no. 1, p. 314, 2025.

M. B. Bhilavade et al., “Fingerprint reconstruction: Approaches to improve fingerprint images,” Journal of Web Engineering and User Applications, 2024.

M. Ali, C. Wang, and M. A., “An efficient convolutional neural network for fingerprint pore detection,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 3, pp. 456–467, 2021.

S. Saponara, A. Elhanashi, and Q. Zhang, “Recreating fingerprint images by convolutional neural network autoencoder architecture,” IEEE Access, vol. 9, pp. 123456–123467, 2021.

E. M. Cherrat, R. Alaoui, and H. Bouzahir, “Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images,” PeerJ Computer Science, vol. 6, p. e248, 2020.

I. Goel, N. Puhan, and B. Mohanty, “Deep convolutional neural network for double-identity fingerprint detection,” IEEE Signal Processing Letters, vol. 27, pp. 1234–1238, 2020.

A. Sengupta and R. Chakraborty, “Secured convolutional layer IP core in convolutional neural network using facial biometric,” IEEE Transactions on Consumer Electronics, vol. 68, no. 3, pp. 345–352, 2022.