Convolution Neural Network Based Method for Biometric Recognition
Keywords:
Ear recognition, machine learning, features extraction, convolutional neural networks, neural network
Abstract
In recent years, there has been increasing interest in the potential of precisely identifying individuals through ear images within the biometric community, owing to the distinctive characteristics of the human ear. This paper introduces deep neural network architecture for ear recognition. The suggested method incorporates a preprocessing stage that enhances significant features in ear images through contrast-limited adaptive histogram equalization. Subsequently, a classifier with deep convolutional neural network is employed to recognize the preprocessed ear images. Experimental results demonstrate a remarkable testing accuracy of 97.92% for the proposed recognition system.
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References
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2. I. Boucherit, M. Zmirli, H. Hentabli, and B. Rosdi, “Finger vein identification using deeply-fused Convolutional Neural Network,” Journal of King Saud University - Computer and Information Sciences, 2020.
3. B. Pandya, G. Cosma, A. A. Alani, A. Taherkhani, V. Bharadi and T.M. McGinnity, "Fingerprint classification using a deep convolutional neural network," 2018 4th International Conference on Information Management (ICIM), pp. 86-91, 2018.
4. C. Chen, J. Ding and C. Huang, "Advanced Ear Detection Algorithm Using Faster R-CNN, Refocus Filters, and the Gradient Map," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), pp. 1-5, 2018.
5. H. Alshazly et al. “Handcrafted versus cnn features for ear recognition,”. Symmetry 11(12), 2019.
6. S. Jia et al., "3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16, 2022.
7. H. Yu, H. Zhang, Y. Liu, K. Zheng, Z. Xu and C. Xiao, "DualChannel Convolution Network With Image-Based Global Learning Framework for Hyperspectral Image Classification," in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022.
8. M. S. Patil and P. B. Mane, "Fused Image Classification using Pre-trained Deep Convolutional Neural Networks," 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 2023, pp. 1215-1221.
9. Fadhil Kadhim Zaidan et al. “Ear Recognition System Based on CLAHE and Convolution Neural Network”, International Journal of Computer Science & Wireless Security (IJCSWS), Vol. 09(03), May-June 2022, pp.04-09.
10. J. Shen et al., "Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-13, 2022.
11. M. K. Roy and U. Keshwala, "Res2Net based Text Independent Speaker recognition system," 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 612-616, 2022.
12. M. Reza Obeidavi and K. Maghooli, "Tumor Detection in Brain MRI using Residual Convolutional Neural Networks," 2022 International Conference on Machine Vision and Image Processing (MVIP), pp.1-5, 2022
13. M. Ponika, K. Jahnavi, P. S. V. S. Sridhar and K. Veena, "Developing a YOLO based Object Detection Application using OpenCV," 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2023, pp. 662-668.
14. M. Prakash, M. Janarthanan and D. Devi, "Multiple Objects Identification for Autonomous Car using YOLO and CNN," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp. 597-601.
15. B. Bai, W. Fu, T. Lu and S. Li, "Edge-Guided Recurrent Convolutional Neural Network for Multitemporal Remote Sensing Image Building Change Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022.
16. S. Taertulakarn, C. Pintavirooj, P. Tosranon and K. Hamamoto, "The preliminary investigation of ear recognition using hybrid technique," 2016 9th Biomedical Engineering International Conference (BMEiCON), pp. 1-4, 2016.
17. M. Chowdhury, R. Islam and J. Gao, "Robust ear biometric recognition using neural network," 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1855-1859, 2017.
18. S. M. Jiddah and K. Yurtkan, "Fusion of Geometric and Texture Features For Ear Recognition," 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-5, 2018.
19. N. Petaitiemthong, P. Chuenpet, S. Auephanwiriyakul and N. TheeraUmpon, "Person Identification from Ear Images Using Convolutional Neural Networks," 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), pp. 148-151, 2019.
20. S. Nikose and H. K. Meena, "Ear-biometrics for human identification," 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), pp. 8-13, 2020.
21. A. Hamdany, A. Ebrahem, and A. Alkababji, “Earprint recognition using deep learning technique,” TELKOMNIKA Telecommunication, Computing, Electronics and Control, Vol. 19, No. 2, pp. 432-437, April 2021.
22. A. Ajit, K. Acharya and A. Samanta, "A Review of Convolutional Neural Networks," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1-5, 2020.
23. Phung, V.H.; Rhee, E.J. “A High-Accuracy Model Average Ensemble of Convolutional Neural Networks for Classification of Cloud Image Patches on Small Datasets”. Appl. Sci. 2019.
24. K. Singh, A. Seth, H. S. Sandhu and K. Samdani, "A Comprehensive Review of Convolutional Neural Network based Image Enhancement Techniques," 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1-6, doi: 10.1109/ICSCAN.2019.8878706.
25. B. Biswas and R. K. Yadav, "A Review of Convolutional Neural Network-based Approaches for Disease Detection in Plants," 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 2023, pp. 514-518, doi: 10.1109/IDCIoT56793.2023.10053428.
26. Palak and A. L. Sangal, "Traffic Signs Classification using Convolutional Neural Networks: A review," 2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC), Jalandhar, India, 2021, pp. 450-455, doi: 10.1109/ICSCCC51823.2021.9478172.
27. S. O. Oppong, F. Twum, J. B. Hayfron-Acquah and Y. M. Missah, "Hybrid Approach to Medicinal Plant Leaf Image Enhancement Using Contrast Limited Adaptive Histogram Equalization and Homomorphic Filter," 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Cape Town, South Africa, 2021, pp. 1-6, doi:
10.1109/ICECET52533.2021.9698460.
28. M. S. Hitam, E. A. Awalludin, W. N. Jawahir Hj Wan Yussof and Z. Bachok, "Mixture contrast limited adaptive histogram equalization for underwater image enhancement," 2013 International Conference on Computer Applications Technology (ICCAT), Sousse, Tunisia, 2013, pp. 1-5, doi: 10.1109/ICCAT.2013.6522017.
29. A. Harsoyo, M. C. Rezi and P. H. Rusmin, "Design of face recognition system using local binary pattern and CLAHE on Smart Meeting Room System," 2013 IEEE 3rd International Conference on System Engineering and Technology, pp. 341-345, 2013.
30. P. Musa, F. A. Rafi and M. Lamsani, "A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition," 2018 Third International Conference on Informatics and Computing (ICIC), pp. 1-6, 2018.
Published
2023-08-25
How to Cite
Yousif, A. J. (2023). Convolution Neural Network Based Method for Biometric Recognition. Central Asian Journal of Theoretical and Applied Science, 4(8), 58-68. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1257
Section
Articles
