Optimization Techniques for Human Multi-Biometric Recognition System
DOI:
https://doi.org/10.29304/jqcsm.2023.15.31292Keywords:
Biometric system, Feature selection, Metaheuristic optimization algorithms, Machine learningAbstract
Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model was evaluated on the SDUMLA-HMT dataset, which contains a variety of biometric features from various individuals. The system outperformed existing techniques in the literature with an excellent recognition accuracy of 99.4%. Although this result is encouraging, further research on larger and more varied datasets is necessary to confirm its applicability across many circumstances. This study highlights how multimodal biometrics strengthened by metaheuristic algorithms can considerably increase biometric security against spoofing assaults, thereby opening a promising new direction for future development in the field.
Downloads
References
K. Nandakumar, A. K. Jain, and A. Ross, Introduction to Biometrics. Springer, 2011. [Online]. Available: https://doi.org/10.1007/978-0-387-77326-1
A. K. Jain, K. Nandakumar, and A. Ross, “50 years of biometric research: Accomplishments, challenges, and opportunities,” Pattern Recognit. Lett., vol. 79, pp. 80-105, 2016.
S. Vishwas and L. D. Resolution, E-Handbook Sabka Vishwas (Legacy Dispute Resolution) Scheme, 2019.
A. Ross, “Biometrics: An Overview,” in Handbook of Biometrics for Forensic Science, M. Tistarelli and C. Champod, Eds., Cham, Switzerland: Springer, 2019.
A. BcH.N. Prakash, “Multimodal Biometric Recognition System Using Face and Finger Vein Biometric Traits with Feature and Decision Level Fusion Techniques,” International Journal of Computer Theory and Engineering, vol. 13(4), pp. 123-128, Sep. 2021.
M. Soltane, S. Bakhti, and M. Soltane, “Multi-modal biometric authentication: Concept issues and application strategies,” International Journal of Computer Theory and Engineering, vol. 48(3), pp. 23-60, 2012.
M. O. Oloyede and G. P. Hancke, “Unimodal and Multimodal Biometric Sensing Systems: A Review,” IEEE Access, vol. 4, pp. 7532-7555, 2016.
B. Ammour et al., “Face–iris multimodal biometric identification system,” Electronics, vol. 9(1), p. 85, Jan. 2020.
H. Zhang and Z. Yang, “Biometric Authentication and Correlation Analysis Based on CNN-SRU Hybrid Neural Network Model,” Computational Intelligence and Neuroscience, vol. 2023, Article ID 8389193.
D. Chang et al., “BIOFUSE: A framework for multi-biometric fusion on bio cryptosystem level,” Information Sciences, vol. 546, pp. 481-511, 2021.
M. Garg et al., “A novel feature biometric fusion approach for iris, speech and signature,” Computer Methods in Material Science, vol. 20(2), pp. 101-109, 2020.
S.Sengar et al., “Multimodal biometric authentication system using deep learning method,” in 2021 International Conference on Circuits, Controls and Communications (CCUBE), pp:1-6.
R.Vinothkanna et al., “A multimodal biometric approach for the recognition of finger print, palm print and hand vein using fuzzy vault,” International Journal of Biometrics, vol:12(3), pp:233–250.
T.Kumar et al., “An Improved Biometric Fusion System of Fingerprint and Face using Whale Optimization,” in 2021 International Conference on Communication Computing and Electronics Systems (ICCCES).
B.O.Mohammed et al., “New feature-level algorithm for a face-fingerprint integral multi-biometrics identification system,” UHD Journal of Science and Technology, vol:6(1), pp:12–20.
Y. Yin, L. Liu, and X. Sun, “SDUMLA-HMT: A multimodal biometric database,” in Proceedings of the 5th International Conference on Biometrics (ICB), 2012, pp. 260-268.
J. Fierrez et al., “BiosecurID: a multimodal biometric database,” arXiv preprint arXiv:2111.03472, Nov. 2021. [Online]. Available: arXiv.org.
I. Oztel, G. Oztel, and D. Akgun, “A hybrid LBP-DCNN based feature extraction method in YOLO: An application for masked face and social distance detection,” Multimedia Tools and Applications, vol. 82(1), pp. 1565-1583, 2022.
O. Lahdenoja, J. Poikonen, and M. Laiho, “Towards understanding the formation of uniform local binary patterns,” International Scholarly Research Notices, vol. 2013, Article ID 429347.
M. A. Rahim et al., “Face recognition using local binary patterns (LBP),” Global Journal of Computer Science and Technology, vol. 13(4), pp. 1-6, 2013.
N. Ansari, “A survey on feature selection techniques using evolutionary algorithms,” Iraqi Journal of Science, vol. 62(8), pp. 2796-2812, 2021.
N. A. K. Hussein and B. Al-Sarray, “Deep learning and machine learning via a genetic algorithm to classify breast cancer DNA data,” Iraqi Journal of Science, vol. 63(7), pp. 3153-3168, 2022.
S.Katoch et al., “A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications, vol:80, pp:8091–8126.
D.Whitley and A.M.Sutton, “Genetic Algorithms — A Survey of Models and Methods,” in Genetic Algorithms: A Comprehensive Introduction, Springer.
S.Katoch et al., “A review on genetic algorithm: past, present, and future,” International Journal of Computer Science and Information Technology, vol:8(2), pp:1-10.
K.K.Srinivasan, “Genetic Algorithms | SpringerLink.”
P.Wang et al., “Application of K-Nearest Neighbor (KNN) Algorithm for Human Action Recognition,” in 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC).
“Particle Swarm Optimization: A Comprehensive Survey,” in IEEE Access, vol:10.
A.G.Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” in Archives of Computational Methods in Engineering.
“Improvement of Particle Swarm Optimization Focusing on Diversity of the Particle Swarm,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 11-14 October 2020. [Online]. Available:
S. Sengupta, S. Basak, and R. A. Peters II, “Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives,” ARXIV PREPRINT ARXIV:1804.05319, 2018. [Online]. Available:
R. A. Ibrahim et al., “Improved salp swarm algorithm based on particle swarm optimization for feature selection,” J. Ambient Intell. Humaniz. Comput., vol. 10, no. 8, pp. 3155–3169, Aug. 2019.
M. Dorigo and G. Di Caro, “Ant colony optimization: a new meta-heuristic,” in Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 1999.
M. Dorigo, “Ant colony optimization,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28-39, Nov. 2006.
“Improving Ant Colony Optimization Efficiency for Solving Combinatorial Optimization Problems,” arXiv:2203.02228 [cs.NE], Mar. 2023.
H.R.Kanan et al., “Feature selection using ant colony optimization (ACO): a new method and comparative study in the application of face recognition system.” Industrial conference on data mining. Springer, Berlin, Heidelberg, 2007.
T.Vijayakumar, “Synthesis of palm print in feature fusion techniques for multimodal biometric recognition system online signature,” J. Innov. Image Process., vol:3(2), pp:131–143.
A.B.Channegowda et al., “Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learnin… Indonesian.pdf.” 2021.
T.Evgeniou et al., “Support Vector Machines: Theory and Applications,” in L.Wang (ed.), Encyclopedia of Machine Learning, vol:1, Springer, Cham, 2019.
S.Saikin et al., “Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results.” JISA (Jurnal Informatika dan Sains), vol:4(1), pp:1-10.
B.Gaye et al., “Improvement of Support Vector Machine Algorithm in Big Data Background.” Mathematical Problems in Engineering, Article ID 5594899.
S.Theodoridis et al., “Support Vector Machines for Classification,” in Pattern Recognition, 5th ed., Cambridge, MA, USA: Academic Press, ch:5, pp:211-282.
Y.Zhang, “Support vector machine classification algorithm and its application.” International conference on information computing and applications. Springer, Berlin, Heidelberg, 2012.
J.Shreffler et al., Diagnostic Testing Accuracy: Sensitivity, Specificity, Predictive Values and Likelihood Ratios. StatPearls Publishing LLC; 2023.
J.Brownlee, “How to Calculate Precision, Recall, and F-Measure for Imbalanced Classification,” Machine Learning Mastery, Jan. 2020.
A.B.Channegowda et al., “Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning,” in 2021 11th International Conference on Computing Communication and Networking Technologies (ICCCNT).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Maryam Yaseen Abdullah
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
