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SETSCI - Volume 6(1) (2023)
BMYZ2023 - Cognitive Models and Artificial Intelligence Conference, Ankara, Türkiye, Oct 26, 2023

Palmprint Recognition Using Pre-Trained Convolutional Neural Networks
Nader Ebrahimpour1*, Faruk Baturalp Günay2
1Papilon Savunma, Ankara, Türkiye
2Atatürk University, Erzurum, Türkiye
* Corresponding author: nebrahimpour@ktu.edu.tr
Published Date: 2023-11-26   |   Page (s): 51-54   |    79     9
https://doi.org/10.36287/setsci.6.1.018
ABSTRACT Palmprint recognition (PR) has garnered significant interest due to its distinctive characteristics and potential applications as a robust and secure biometric authentication (BA) method across various domains. This paper introduces a novel approach to enhance PR using pre-trained Convolutional Neural Network (CNN) models. The proposed method process harnesses the capabilities of Deep Learning (DL) and Transfer Learning (TL) to improve palmprint recognition by leveraging state-of-the-art CNN architectures for feature extraction and classification. This proposed method begins by exploring the potential of pre-trained CNN architectures, including ShuffleNet, EfficientNet, and MobileNet, as feature extractors for palmprints. In the next step, extracted feature vectors are compared using the Cosine Similarity (CS) method. The proposed method is thoroughly evaluated through comprehensive experiments. Results of evaluations demonstrate that pre-trained CNN models excel at recognizing palmprints for biometric authentication, establishing their proficiency in this domain. Consequently, this paper illuminates the inherent capabilities of pre-trained CNN models as a potent tool for advancing PR, introducing an innovative facet to BA methodologies.
KEYWORDS Palmprint Recognition, Convolutional Neural Networks, Biometric Authentication
REFERENCES S.-Y. Jeon and M.-K. Lee, "Acceleration of inner-pairing product operation for secure biometric verification," Sensors, vol. 21, no. 8, p. 2859, 2021.

S. Zhao and B. Zhang, "Learning salient and discriminative descriptor for palmprint feature extraction and identification," IEEE transactions on neural networks and learning systems, vol. 31, no. 12, pp. 5219-5230, 2020.

Y. Fan, J. Li, S. Song, H. Zhang, S. Wang, and G. Zhai, "Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning," Phenomics, vol. 2, no. 4, pp. 219-229, 2022.

J. Dai and J. Zhou, "Multifeature-based high-resolution palmprint recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 945-957, 2010.

N. Ebrahimpour, "Handwritten Signatures Forgery Detection Using Pre-Trained Deep Learning Methods," presented at the International Congress of New Horizons in Sciences, İstanbul/Türkiye, 2023, 224-229.

L. Zhang, Z. Cheng, Y. Shen, and D. Wang, "Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset," Symmetry, vol. 10, no. 4, p. 78, 2018.

W. M. Matkowski and A. W. K. Kong, "Gender and ethnicity classification based on palmprint and palmar hand images from uncontrolled environment," in 2020 IEEE International Joint Conference on Biometrics (IJCB), 2020: IEEE, pp. 1-7.

Y. Aberni, L. Boubchir, and B. Daachi, "Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization," Pattern Recognition Letters, vol. 136, pp. 101-110, 2020.

S.-Y. Jhong et al., "An automated biometric identification system using CNN-based palm vein recognition," in 2020 international conference on advanced robotics and intelligent systems (ARIS), 2020: IEEE, pp. 1-6.

M. Stanuch, M. Wodzinski, and A. Skalski, "Contact-free multispectral identity verification system using palm veins and deep neural network," Sensors, vol. 20, no. 19, p. 5695, 2020.

S. B. Jemaa and M. Hammami, "Human Identification Based on the Palmar Surface of the Hand," in 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), 2016: IEEE, pp. 51-56.

A. Morales, A. Kumar, and M. A. Ferrer, "Interdigital palm region for biometric identification," Computer Vision and Image Understanding, vol. 142, pp. 125-133, 2016.

A. Betancourt, P. Morerio, E. Barakova, L. Marcenaro, M. Rauterberg, and C. Regazzoni, "Left/right hand segmentation in egocentric videos," Computer Vision and Image Understanding, vol. 154, pp. 73-81, 2017.

N. Ebrahimpour, M. A. Ayden, and B. Altay, "Liveness control in face recognition with deep learning methods," The European Journal of Research and Development, vol. 2, no. 2, pp. 92-101, 2022.

N. Ebrahimpour, "Iris Recognition Using Mobilenet For Biometric Authentication," presented at the 9th INTERNATIONAL ZEUGMA CONFERENCE ON SCIENTIFIC RESEARCH, Gaziantep, Turkey, 2023.

H. Chen et al., "Pre-trained image processing transformer," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 12299-12310.

X. Zhang, X. Zhou, M. Lin, and J. Sun, "Shufflenet: An extremely efficient convolutional neural network for mobile devices," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6848-6856.

M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," in International conference on machine learning, 2019: PMLR, pp. 6105-6114.

A. G. Howard et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.

A. R. Lahitani, A. E. Permanasari, and N. A. Setiawan, "Cosine similarity to determine similarity measure: Study case in online essay assessment," in 2016 4th International Conference on Cyber and IT Service Management, 2016: IEEE, pp. 1-6.

Leosocy. "A robust algorithm for extracting ROI from palm image taken by mobile phone." https://github.com/leosocy/RobustPalmRoi (accessed.

L. Zhang, L. Li, A. Yang, Y. Shen, and M. Yang, "Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach," Pattern Recognition, vol. 69, pp. 199-212, 2017.

E. Stevens, L. Antiga, and T. Viehmann, Deep learning with PyTorch. Manning Publications, 2020.

D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.


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