Multi-Level Pooling Model for Fingerprint-Based Gender Classification
Abstract
It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results in classifying images. The strength of CNN lies in the type and the number of layers that construct it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and its lengthy training time. Although datasets are available, labeling that data is a significant problem. This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is removing convolution layers and directly processing fingerprint images using pooling layers. Three pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used to generate fingerprint features to classify their owner gender. These pooling layers are arranged consecutively up to eight levels. Removing convolution layers makes the process straightforward, and the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed method resulted in an accuracy of 61% to 71.5% or an average of 66.25%.
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References
[2] T. Philipp, D. Naser, B. Andreas, and K. Arjan, “Minutiae-Based Gender Estimation for Full and Partial Fingerprints of Arbitrary
Size and Shape,” in Asian Conference on Computer Vision, vol. 11361 LNCS, 2019, pp. 171–186.
[3] R. B. Ali, R. Ejbali, and M. Zaied, “A Deep Convolutional Neural Wavelet Network for Classification of Medical Images,”
Journal of Computer Science, vol. 14, no. 11, pp. 1488–1498, 2018.
[4] M. Harahap, A. P. S. Pasaribu, D. R. Sinaga, and R. Sipangkar, “Classification of Tuberculosis Based on Lung X-Ray Image
With Data Science Approach Using Convolutional Neural Network,” SinkrOn, vol. 7, no. 4, pp. 2193–2197, 2022.
[5] M. O. Arowolo, M. O. Adebiyi, E. P. Michael, H. E. Aigbogun, S. O. Abdulsalam, and A. A. Adebiyi, “Detection of COVID-
19 from Chest X-Ray Images using CNN and ANN Approach,” International Journal of Advanced Computer Science and
Applications, vol. 13, no. 6, pp. 754–759, 2022.
[6] N. Alsharman and I. Jawarneh, “GoogleNet CNN neural network towards chest CT-coronavirus medical image classification,”
Journal of Computer Science, vol. 16, no. 5, pp. 620–625, 2020.
[7] R. Davuluri and R. Rengaswamy, “Improved Classification Model using CNN for Detection of Alzheimer’s Disease,” Journal
of Computer Science, vol. 18, no. 5, pp. 415–425, 2022.
[8] V. Venkatesh, N. Yallappa, S. U. Hegde, and S. R. Stalin, “Fine-Tuned MobileNet Classifier for Classification of Strawberry
and Cherry Fruit Types,” Journal of Computer Science, vol. 17, no. 1, pp. 44–54, 2021.
[9] S. Jatmika and D. E. Saputra, “Rice Plants Disease Identification Using Deep Learning with Convolutional Neural Network
Method,” SinkrOn, vol. 7, no. 3, pp. 2008–2016, 2022.
[10] A. A. Gomaa and Y. M. El-Latif, “Early Prediction of Plant Diseases using CNN and GANs,” International Journal of Advanced
Computer Science and Applications, vol. 12, no. 5, pp. 514–519, 2021.
[11] K. Singh, R. Scholar, A. Mahajan, and V. Mansotra, “1D-CNN based Model for Classification and Analysis of Network Attacks,”
International Journal of Advanced Computer Science and Applications, vol. 12, no. 11, pp. 604–613, 2021.
[12] B. K. Triwijoyo, “Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender
Berdasarkan Citra Wajah,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 18, no. 2, pp.
211–221, 2019.
[13] B. K. Triwijoyo, A. Adil, and A. Anggrawan, “Convolutional Neural Network With Batch Normalization for Classification of
Emotional Expressions Based on Facial Images,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer,
vol. 21, no. 1, pp. 197–204, 2021.
[14] T. A. Zuraiyah, S. Maryana, and A. Kohar, “Automatic Door Access Model Based on Face Recognition using Convolutional
Neural Networ,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 1, pp. 241–252, 2022.
[15] N. D. Miranda, L. Novamizanti, and S. Rizal, “Convolutional Neural Network Pada Klasifikasi Sidik Jari Menggunakan Resnet-
50,” Jurnal Teknik Informatika (Jutif), vol. 1, no. 2, pp. 61–68, 2020.
[16] I. Boucherit, M. O. Zmirli, H. Hentabli, and B. A. Rosdi, “Finger Vein Identification Using Deeply-Fused Convolutional Neural
Network,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 3, pp. 646–656, 2022.
[17] H. Hariyanto, S. Madenda, S. A. Sudiro, and T. M. Kusuma, “Fingerprint Authenticity Classification Algorithm based-on
Distance of Minutiae using Convolutional Neural Network,” Jurnal Telekomunikasi dan Komputer, vol. 11, no. 3, pp. 243–253,
2021.
[18] C. Lin and A. Kumar, “A CNN-Based Framework for Comparison of Contactless to Contact-Based Fingerprints,” IEEE Transactions
on Information Forensics and Security, vol. 14, no. 3, pp. 662–676, 2019.
[19] M. Aritonang, I. D. Hutahaean, H. Sipayung, and I. H. Tambunan, “Implementation of Fingerprint Recognition Using Convolutional
Neural Network and RFID Authentication Protocol on Attendance Machine,” ACM International Conference Proceeding
Series, pp. 151–156, 2020.
[20] B. Rim, J. Kim, and M. Hong, “Gender Classification from Fingerprint-images using Deep Learning Approach,” ACM International
Conference Proceeding Series, pp. 7–12, 2020.
[21] W. Alakwaa, M. Nassef, and A. Badr, “Lung Cancer Detection and Classification with 3D Convolutional Neural Network
(3D-CNN),” International Journal of Biology and Biomedical Engineering, vol. 11, no. 8, pp. 66–73, 2017.
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