The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture

 (*)Diah Mitha Aprilla Mail (Universitas Mataram, Mataram, Indonesia)
 Fitri Bimantoro (Universitas Mataram, Mataram, Indonesia)
 I Gede Pasek Suta Wijaya (Universitas Mataram, Mataram, Indonesia)

(*) Corresponding Author

Submitted: March 23, 2024; Published: April 30, 2024

Abstract

The palmprint is a part of the human body that has unique and detailed characteristics of the pattern of palm lines, such as the length and width of the palm (geometric features), principal lines, and wrinkle lines. It began to be developed as a tool for recognize a person. The palmprint dataset used comes from Kaggle, namely BMPD. The palmprint images in this dataset were taken in 2 sessions. In the first session, there was not much variation in rotation compared to the second session. This research uses Convolutional Neural Network (CNN) models with Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 architectures to see the best performance. The results of this research showed that the MobileNet model had the best performance with an accuracy of 96.6% and a loss of 14.3%. For Precision results of 94%, Recall 96%, and F1-Score 94%. Meanwhile, Xception obtained an accuracy of 88.3% and a loss of 52.9%, VGG16 70.8% and a loss of 109.8%, ResNet50 5.8% and a loss of 307.9%, and EfficientNetB0 3.3% and a loss of 340.1%.

Keywords


Palmprint; Biometrics; Convolutional Neural Network; Image Classification; CNN Architecture

Full Text:

PDF


Article Metrics

Abstract view : 162 times
PDF - 53 times

References

D. Sely Wita and D. Yanti Liliana, “Klasifikasi Identitas Dengan Citra Telapak Tangan Menggunakan Convolutional Neural Network (CNN),” JURTI, vol. 6, no. 1, 2022, doi: 10.30872/jurti.v6i1.7100.

N. Fadillah and D. Lestari, “HAND HUMAN RECOGNITION BERDASARKAN GEOMETRI TELAPAK TANGAN MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS,” Jurnal SIMETRIS, vol. 10, no. 2, 2019.

G. T. Situmorang, A. W. Widodo, and M. A. Rahman, “Penerapan Metode Gray Level Cooccurence Matrix (GLCM) untuk Ekstraksi Ciri pada Telapak Tangan,” J-PTIIK, vol. 3, no. 5, pp. 4710–4716, 2019.

N. Hardi and J. Sundari, “Pengenalan Telapak Tangan Menggunakan Convolutionall Neural Network (CNN),” Jurnal Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 10-15, 2022, doi: http://dx.doi.org/10.31294/reputasi.v4i1.1951.

F. A. Breve, “COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles,” Expert Systems With Applications, vol. 204, p. 117549, Oct. 2022, doi: 10.1016/j.eswa.2022.117549.

M. Shorfuzzaman and M. Masud, “On the detection of covid-19 from chest x-ray images using cnn-based transfer learning,” Computers, Materials and Continua, vol. 64, no. 3, pp. 1359–1381, Jun. 2020, doi: 10.32604/cmc.2020.011326.

M. Izadpanahkakhk, S. M. Razavi, M. Taghipour-Gorjikolaie, S. H. Zahiri, and A. Uncini, “Novel mobile palmprint databases for biometric authentication,” International Journal of Grid and Utility Computing, vol. 10, no. 5, pp. 465–474, 2019, doi: 10.1504/IJGUC.2019.102016.

L. Alzubaidi et al., “Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study,” PeerJ Comput Sci, vol. 7, pp. 1–27, 2021, doi: 10.7717/peerj-cs.715.

S. Roopashree and J. Anitha, “DeepHerb: A Vision Based System for Medicinal Plants Using Xception Features,” IEEE Access, vol. 9, pp. 135927–135941, 2021, doi: 10.1109/ACCESS.2021.3116207.

K. Shaheed et al., “DS-CNN: A pre-trained Xception model based on depth-wise separable convolutional neural network for finger vein recognition,” Expert Syst Appl, vol. 191, p. 116288, Apr. 2022, doi: 10.1016/J.ESWA.2021.116288.

R. Mohan, K. Ganapathy, and A. Rama, “Brain tumour classification of magnetic resonance images using a novel CNN-based medical image analysis and detection network in comparison to VGG16,” Journal of Population Therapeutics and Clinical Pharmacology, vol. 28, no. 2, pp. e113–e125, 2021, doi: 10.47750/jptcp.2022.873.

L. Kong and J. Cheng, “Classification and detection of COVID-19 X-Ray images based on DenseNet and VGG16 feature fusion,” Biomed Signal Process Control, vol. 77, Aug. 2022, doi: 10.1016/j.bspc.2022.103772.

W. Bakasa and S. Viriri, “VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction,” J Imaging, vol. 9, no. 7, Jul. 2023, doi: 10.3390/jimaging9070138.

A. A. Alnuaim et al., “Speaker Gender Recognition Based on Deep Neural Networks and ResNet50,” Wirel Commun Mob Comput, vol. 2022, pp. 1–13, Mar. 2022, doi: 10.1155/2022/4444388.

R. Zhang, Y. Zhu, Z. Ge, H. Mu, D. Qi, and H. Ni, “Transfer Learning for Leaf Small Dataset Using Improved ResNet50 Network with Mixed Activation Functions,” Forests, vol. 13, no. 12, Dec. 2022, doi: 10.3390/f13122072.

A. A. Asiri et al., “Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and U-Net Model: A Study on TCGA-LGG and TCIA Dataset for MRI Applications,” Life, vol. 13, no. 7, Jul. 2023, doi: 10.3390/life13071449.

A. Rifa, I. Sujiwanto, R. Ronggo Bintang Pratomo Prawirodirjo, and P. Palupingsih, “Analisis Perbandingan Performa Model Klasifikasi Kesehatan Daun Tomat menggunakan Arsitektur VGG, MobileNet, dan Inception V3 Analysis Tomato Leaf Health Classification Model Performance Comparison Using VGG, MobileNet, and Inception V3,” Jurnal Ilmu Komputer Dan Agri-Informatika, vol. 10, no. 1, pp. 98–110, Jun. 2023, doi: 10.29244/jika.10.1.98-110.

R. K. Shukla and A. K. Tiwari, “Masked Face Recognition Using MobileNet V2 with Transfer Learning,” Computer Systems Science and Engineering, vol. 45, no. 1, pp. 293–309, 2023, doi: 10.32604/csse.2023.027986.

M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” arXiv (Cornell University), May 2019, [Online]. Available: https://arxiv.org/pdf/1905.11946v5.

P. E. N. Taruno, G. S. Nugraha, R. Dwiyansaputra, and F. Bimantoro, “Monkeypox Classification based on Skin Images using CNN: EfficientNet-B0,” E3S Web of Conferences, vol. 465, p. 02031, Jan. 2023, doi: 10.1051/e3sconf/202346502031.

R. H. Jatmiko and Y. Pristyanto, “Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification,” MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 1, pp. 1–16, Oct. 2023, doi: 10.30812/matrik.v23i1.3185.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel The Palmprint Recognition Using Xception, VGG16, ResNet50, MobileNet, and EfficientNetB0 Architecture

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.