DETECTION OF ADOLESCENCE USING FINGRPRINTS
Main Article Content
Keywords
Fingerprint, Optimal Light weight CNN hybrid model
Abstract
To realize the design efficacies related to fingerprint biometric systems with different ages groups multiple research developments have been introduced to implicate the design criteria. The current design on CNN or LSTM or SOA architectures have crude impacts on how to optimize the images and other functional features of the images considered. To analyze such design criteria, that have implemented an optimal solution with CNN as OLW algorithm to reduce the functional changes on tuning the hyper parameters. The goal of the proposed work is to reduce the number of comparisons in adolescent-based age groups with databases for fingerprints utilized to implicate the overall design with OLW-CNN algorithm. With these featured aspects we tend to implicate the design with performance metrics such as accuracy precision, recall and F1-score parameters to compare the existing approach with the proposed OLW-CNN. With this optimization the overall performance has been observed to reach 100%.
References
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20. J. Zhang, C. Xu, Z. Cao, M. Yang and Z. Zhang, "Research on fingerprint identification of network assets based on CNN-GRU neural network model," 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2022), Hybrid Conference, Hong Kong, China, 2022, pp. 119-123, doi: 10.1049/icp.2023.0085.
21. J. Zhang, H. Su, Y. Li and H. Yang, "Fingerprint Recognition Scheme Based on Deep Learning and Homomorphic Encryption," 2022 3rd International Conference on Information Science and Education (ICISE-IE), Guangzhou, China, 2022, pp. 103-107, doi: 10.1109/ICISE-IE58127.2022.00029.
22. S. H. Mahmood, A. K. Farhan and E. M. El-Kenawy, "A proposed model for fingerprint recognition based on convolutional neural networks," 6th Smart Cities Symposium (SCS 2022), Hybrid Conference, Bahrain, 2022, pp. 343-347, doi: 10.1049/icp.2023.0572.
23. D. Martínez, D. Zabala-Blanco, R. Ahumada-García, C. A. Azurdia-Meza, M. Flores-Calero and P. Palacios-Jativa, "Review of Extreme Learning Machines for the Identification and Classification of Fingerprint Databases," 2022 IEEE Colombian Conference on Communications and Computing (COLCOM), Cali, Colombia, 2022, pp. 1-6, doi: 10.1109/Colcom56784.2022.10107849
2. K. Gu, G. Zhai, X. Yang and W. Zhang, "Using free energy principle for blind image quality assessment", IEEE Trans. Multimedia, vol. 17, no. 1, pp. 50-63, Jan. 2015.
3. K. Ma, W. Liu, K. Zhang, Z. Duanmu, Z. Wang and W. Zuo, "End-to-end blind image quality assessment using deep neural networks", IEEE Trans. Image Process., vol. 27, no. 3, pp. 1202-1213, Mar. 2018.
4. T. Chugh and A. K. Jain, “Fingerprint Spoof Detector Generalization,” in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 42-55, 2021, doi: 10.1109/TIFS.2020.2990789.
5. Handbook of Biometric Anti-Spoofing: Presentation Attack Detection, Cham, Switzerland:Springer, 2019.
6. S. S. Arora, K. Cao, A. K. Jain and N. G. Paulter, "Design and fabrication of 3D fingerprint targets", IEEE Trans. Inf. Forensics Security, vol. 11, no. 10, pp. 2284-2297, Oct. 2016.
7. I. Goicoechea-Telleria, K. Kiyokawa, J. Liu-Jimenez and R. Sanchez-Reillo, "Low-Cost and Efficient Hardware Solution for Presentation Attack Detection in Fingerprint Biometrics Using Special Lighting Microscopes," in IEEE Access, vol. 7, pp. 7184-7193, 2019, doi: 10.1109/ACCESS.2018.2888905.
8. Q. N. Tran, B. P. Turnbull, M. Wang and J. Hu, "A Privacy-Preserving Biometric Authentication System With Binary Classification in a Zero Knowledge Proof Protocol," in IEEE Open Journal of the Computer Society, vol. 3, pp. 1-10, 2022, doi: 10.1109/OJCS.2021.3138332.
9. M. Wang, S. Wang and J. Hu, "Cancellable Template Design for Privacy-Preserving EEG Biometric Authentication Systems," in IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3350-3364, 2022, doi: 10.1109/TIFS.2022.3204222.
10. M. Szymkowski, E. Saeed, M. Omieljanowicz, A. Omieljanowicz, K. Saeed and Z. Mariak, "A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms," in IEEE Access, vol. 8, pp. 125849-125862, 2020, doi: 10.1109/ACCESS.2020.3007656.
11. K. Noor et al., "Performances Enhancement of Fingerprint Recognition System Using Classifiers," in IEEE Access, vol. 7, pp. 5760-5768, 2019, doi: 10.1109/ACCESS.2018.2879272.
12. A. Krishnan and T. Thomas, "Finger Vein Recognition Based on Anatomical Features of Vein Patterns," in IEEE Access, vol. 11, pp. 39373-39384, 2023, doi: 10.1109/ACCESS.2023.3253203.
13. S. S, D. B. Kinthadi and J. Ravindra, "Feature Level Fusion of Face and Fingerprint Biometric Traits for Universality," 2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS), Bangalore, India, 2023, pp. 199-204, doi: 10.1109/ICAECIS58353.2023.10170487.
14. Z. Minjie, Z. Diqing, S. Yan and Z. Yilian, "Fingerprint Identification Technology of Power IOT Terminal based on Network Traffic Feature," 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 2023, pp. 1711-1715, doi: 10.1109/ICIBA56860.2023.10165381.
15. A. Berdich, P. Iosif, C. Burlacu, A. Anistoroaei and B. Groza, "Fingerprinting Smartphone Accelerometers with Traditional Classifiers and Deep Learning Networks," 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, 2023, pp. 000039-000044, doi: 10.1109/SACI58269.2023.10158600.
16. G. Arora, A. Kumbhat, A. Bhatia and K. Tiwari, "CP-Net: Multi-Scale Core Point Localization in Fingerprints Using Hourglass Network," 2023 11th International Workshop on Biometrics and Forensics (IWBF), Barcelona, Spain, 2023, pp. 1-6, doi: 10.1109/IWBF57495.2023.10157521.
17. J. Feng, X. Tang, B. Zhang and Y. Ren, "Lightweight CNN-Based RF Fingerprint Recognition Method," 2023 8th International Conference on Computer and Communication Systems (ICCCS), Guangzhou, China, 2023, pp. 1031-1035, doi: 10.1109/ICCCS57501.2023.10150764.
18. T. Rithanasophon and C. Wannaboon, "Integration of Machine Learning and Kalman Filter Approach for Fingerprint Indoor Positioning," 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Phanom, Thailand, 2023, pp. 1-4, doi: 10.1109/ECTI-CON58255.2023.10153162.
19. S. M. Sutton and J. L. Rrushi, "Decoy Processes With Optimal Performance Fingerprints," in IEEE Access, vol. 11, pp. 43216-43237, 2023, doi: 10.1109/ACCESS.2023.3271999.
20. J. Zhang, C. Xu, Z. Cao, M. Yang and Z. Zhang, "Research on fingerprint identification of network assets based on CNN-GRU neural network model," 12th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2022), Hybrid Conference, Hong Kong, China, 2022, pp. 119-123, doi: 10.1049/icp.2023.0085.
21. J. Zhang, H. Su, Y. Li and H. Yang, "Fingerprint Recognition Scheme Based on Deep Learning and Homomorphic Encryption," 2022 3rd International Conference on Information Science and Education (ICISE-IE), Guangzhou, China, 2022, pp. 103-107, doi: 10.1109/ICISE-IE58127.2022.00029.
22. S. H. Mahmood, A. K. Farhan and E. M. El-Kenawy, "A proposed model for fingerprint recognition based on convolutional neural networks," 6th Smart Cities Symposium (SCS 2022), Hybrid Conference, Bahrain, 2022, pp. 343-347, doi: 10.1049/icp.2023.0572.
23. D. Martínez, D. Zabala-Blanco, R. Ahumada-García, C. A. Azurdia-Meza, M. Flores-Calero and P. Palacios-Jativa, "Review of Extreme Learning Machines for the Identification and Classification of Fingerprint Databases," 2022 IEEE Colombian Conference on Communications and Computing (COLCOM), Cali, Colombia, 2022, pp. 1-6, doi: 10.1109/Colcom56784.2022.10107849
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Published
Oct 31, 2023
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How to Cite
Ch.Manasa , Dr. S. Nagini. (2023). DETECTION OF ADOLESCENCE USING FINGRPRINTS. Journal of Population Therapeutics and Clinical Pharmacology, 30(14), 386–397. Retrieved from https://jptcp.com/index.php/jptcp/article/view/3295
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