Integration of EfficientNetB0 and Machine Learning for Fingerprint Classification

Jenan A. Alhijaj, Raidah S. Khudeyer


A fingerprint is a common form of biometric technology used in human identification. The classification of fingerprints is crucial in identification systems because it significantly reduces the time required to identify a person and allows for the possibility of using fingerprints to distinguish between genders and identify individuals. Fingerprints are the most reliable identifiers because they are unique and impossible to fake. As a method of personal identification, fingerprints remain the best and most trustworthy. Fingerprint classification is crucial in a wide variety of settings, such as airports, banks, and emergencies involving explosives and natural disasters. This study proposes a deep learning strategy for determining whether a fingerprint belongs to a male or female person. With the help of pre-trained convolutional neural networks (CNN) in computer vision and an extremely powerful tool that has achieved significant success in image classification and pattern recognition. This work includes the use of the SOCOFing fingerprint dataset for training and employing a state-of-the-art model for feature extraction called EfficientNetB0, which was trained on the ImageNet dataset. Then feeding the extracted features into a principal component analysis (PCA) to decrease the dimension of these features and random forest RF classifier for fingerprint classification. Lastly, the tests showed that the proposed strategy outperformed the previous categorization methods in terms of accuracy (99.91%), speed for execution time, and efficiency.

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O. Giudice, M. Litrico, and S. Battiato, “Single architecture and multiple task deep neural network for altered fingerprint analysis,” Jul. 2020, [Online]. Ava. available:

M. Diarra, A. K. Jean, B. A. Bakary, and K. B. Medard, “Study of Deep Learning Methods for Fingerprint Recognition,” International Journal of Recent Technology and Engineering (IJRTE), vol. 10, no. 3, pp. 192–197, Sep. 2021, doi: 10.35940/ijrte.C6478.0910321.

N. M. Al-Moosawi and R. S. Khudeyer, “ResNet-34/DR: A Residual Convolutional Neural Network for the Diagnosis of Diabetic Retinopathy,” Informatica (Slovenia), vol. 45, no. 7, pp. 115–124, 2021,

doi: 10.31449/inf.v45i7.3774.

B. K. Oleiwi, L. H. Abood, and A. K. Farhan, “Integrated Different Fingerprint Identification and Classification Systems based Deep Learning,” in Proceedings of the 2nd 2022 International Conference on Computer Science and Software Engineering, CSASE 2022, 2022, pp. 188–193.

doi: 10.1109/CSASE51777.2022.9759632.

C. Yuan, X. Li, Q. M. J. Wu, J. Li, and X. Sun, “Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis,” 2017.

M. D. White, A. Tarakanov, C. P. Race, P. J. Withers, and K. J. H. Law, “Digital Fingerprinting of Microstructures,” Mar. 2022, [Online]. Available:

R. S. Khudeyer and N. M. Almoosawi, “Combination of machine learning algorithms and Resnet50 for Arabic Handwritten Classification,” Informatica, vol. 46, no. 9, Jan. 2023, doi: 10.31449/inf.v46i9.4375.

Y. I. Shehu, A. Ruiz-Garcia, V. Palade, and A. James, “Detection of fingerprint alterations using deep convolutional neural networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11139 LNCS, pp. 51–60. doi: 10.1007/978-3-030-01418-6_6.

Y. I. Shehu, A. Ruiz-Garcia, V. Palade, and A. James, “Detailed Identification of Fingerprints Using Convolutional Neural Networks,” in Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Jan. 2019, pp. 1161–1165. doi: 10.1109/ICMLA.2018.00187.

O. Giudice, M. Litrico, and S. Battiato, “Single architecture and multiple task deep neural network for altered fingerprint analysis,” Jul. 2020, [Online]. Available:

J. Fattahi and M. Mejri, “Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes,” Dec. 2020, [Online]. Available:

D. Moga and I. Filip, “Study on fingerprint authentication systems using convolutional neural networks,” in SACI 2021 - IEEE 15th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, May 2021, pp. 15–20.

doi: 10.1109/SACI51354.2021.9465628.

F. B. Ibitayo, O. A. Olanrewaju, and M. B. Oyeladun, “A FINGERPRINT BASED GENDER DETECTOR SYSTEM USING FINGERPRINT PATTERN ANALYSIS,” international journal of advanced research in computer science, vol. 13, no. 4, pp. 35–47, Aug. 2022, doi: 10.26483/ijarcs.v13i4.6885.

Y. Al-Wajih, W. Hamanah, M. Abido, F. Al-Sunni, and F. Alwajih, “Finger Type Classification with Deep Convolution Neural Networks,” Jul. 2022, pp. 247–254.

doi: 10.5220/0011327100003271.

R. Sravanthi and R. Sabitha, “Improving the Efficiency of Fingerprint Verification Using Support Vector Machine (SVM) in Comparison with Naïve Bayes Classifier.” [Online]. Available:

D. Ganesh, D. Akshitha, C. Gayathri, and S. Sujana, “Fingerprint Image Identification for Crime Detection using Convolutional neural networks,” in 2022 3rd International Conference for Emerging Technology, INCET 2022, 2022.

doi: 10.1109/INCET54531.2022.9824388.

Y. Isah Shehu, A. Ruiz-Garcia, V. Palade, and A. James, “Sokoto Coventry Fingerprint Dataset.” [Online]. Available:

T. Singh, S. Bhisikar, Satakshi, and M. Kumar, “Fingerprint Identification using Modified Capsule Network,” in 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, 2021.


P. Tertychnyi, C. Ozcinar, and G. Anbarjafari, “Low-quality fingerprint classification using deep neural network,” IET Biom, vol. 7, no. 6, pp. 550–556, Nov. 2018,

doi: 10.1049/iet-bmt.2018.5074.

R. M. Jomaa, H. Mathkour, Y. Bazi, and M. S. Islam, “End-to-end deep learning fusion of fingerprint and electrocardiogram signals for presentation attack detection,” Sensors (Switzerland), vol. 20, no. 7, Apr. 2020,

doi: 10.3390/s20072085.

M. Tan and Q. v. Le, “EfficientNetV2: Smaller Models and Faster Training,” Apr. 2021, [Online]. Available:

M. Tan and Q. v. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” May 2019, [Online]. Available:

A. M. Alkababji and O. H. Mohammed, “Real time ear recognition using deep learning,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 19, no. 2, pp. 523–530, Apr. 2021,

doi: 10.12928/TELKOMNIKA.v19i2.18322.

S. Aryanmehr and F. Z. Boroujeni, “Efficient deep CNN-based gender classification using Iris wavelet scattering,” Multimed Tools Appl, 2022, doi: 10.1007/s11042-022-14062-w.

S. M. Hassan and A. K. Maji, “Deep feature-based plant disease identification using machine learning classifier,” Innov Syst Softw Eng, 2022, doi: 10.1007/s11334-022-00513-y.

J. Ma and Y. Yuan, “Dimension reduction of image deep feature using PCA,” J Vis Commun Image Represent, vol. 63, Aug. 2019,

doi: 10.1016/j.jvcir.2019.102578.

M. K. Benkaddour and A. Bounoua, “Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition,” Traitement du Signal, vol. 34, no. 1–2, pp. 77–91, 2017,

doi: 10.3166/TS.34.77-91.

S. Ekal, K. Wadke, M. Altamash, and R. Kute, “Face and Fingerprint Fusion Using Deep Learning,” in Lecture Notes in Electrical Engineering, 2023, vol. 959, pp. 155–164.

doi: 10.1007/978-981-19-6581-4_13.

H. T. Nguyen and L. T. Nguyen, “Fingerprints classification through image analysis and machine learning method,” Algorithms, vol. 12, no. 11, Nov. 2019, doi: 10.3390/a12110241.

R. Mostafiz, M. S. Uddin, N. A. Alam, M. Mahfuz Reza, and M. M. Rahman, “Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, pp. 3226–3235, Jun. 2022, doi: 10.1016/j.jksuci.2020.12.010.


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