A framework for Modified Firefly Algorithm in Multimodal Biometric Authentication System
Many end users are turning to multimodal biometric systems as a result of the limitations of conventional authentication techniques and unimodal biometric systems for offering a high level of accurate authentication. When high accuracy and security are required, multimodal biometrics are the best choice because to the utilization of numerous identification modalities. It is difficult to identify the best features that contribute to the recognition rate/accuracy and have a high redundancy of features since different features are acquired at the feature level fusion from a variety of physiological or behavioral variables. At the feature selection level, the utilization of meta-heuristic algorithms will reduce the number of redundant features while keeping critical feature sets that are important to biometric performance, accuracy, and efficiency. The study demonstrated a multimodal biometric authentication system that used the features of the face and both irises. In order to avoid being stuck at the local optimum and hasten convergence, the Firefly Algorithm (FFA) was modified by including a chaotic sinusoidal map function and a roulette wheel selection mechanism as deterministic processes. The results of the study demonstrated that in terms of sensitivity, precision, recognition accuracy, and time, the proposed MFFA with multimodal outperformed the MFFA for unimodal, bi-modal, and bi-instance. In addition to being computationally faster, more accurate, and suitable for real-time applications, the modified method, known as MFFA, proved effective in integrating multimodal data sets.
REFERENCE
O. Al-hamdani et al., “Multimodal Biometrics Based on Identification and Verification System,” J. Biom. Biostat., vol. 4, no. 2, 2013, doi: 10.4172/2155-6180.1000163.
M. Dutta, K. K. Psyche, and S. Yasmin, “ATM Transaction Security Using Fingerprint Recognition American Journal of Engineering Research ( AJER ),” Am. J. Eng. Res., vol. 6, no. 8, pp. 41–45, 2017.
N. Taleb and S. B. Abbes, “A Robust Multi-Biometric System with Compact Code for Iris and Face,” Int. J. Ectrical Eng. Informatics, vol. 10, no. 1, pp. 1–13, 2018, doi: 10.15676/ijeei.2018.10.1.1.
D. N. Bhavya and H. K. Chethan, “Feature and Decision Level Fusion in Children Multimodal Biometrics,” Int. J. Recent Technol. Eng., vol. 8, no. 5, pp. 2522–2527, 2020, doi: 10.35940/ijrte.e6396.018520.
V. Saini, N & Kang, “Comparative Analysis of Iris Recognition Techniques : A Review,” Int. J. Comput. Appl., vol. 2, no. 7, pp. 23–27, 2016.
L. A. Alaslani, M. G & Elrefaei, “Convolutional Neural Network Based Feature Extraction For Iris Recognition,” Int. J. Comput. Inf. Technol., vol. 10, no. 2, pp. 65–78, 2018, doi: 10.5121/ijcsit.2018.10206.
S. M. Prakash, P. Betty, and K. Sivanarulselvan, “Fusion of Multimodal Biometrics using Feature and Score Level Fusion,” Iinternational J. Appl. Inf. Commun. Engineeering, vol. 2, no. 4, pp. 52–56, 2016.
F. Yahya, H. Nasir, and K. Kadir, “Multimodal Biometric Algorithm : A Survey,” Biotechnology, vol. 15, no. 5, pp. 119–124, 2016, doi: 10.3923/biotech.2016.119.124.
H. Jaafar and D. A. Ramli, “A Review of Multibiometric System with FusionStrategies and Weighting Factor,” Int. J. Comput. Sci. Eng., vol. 2, no. 04, pp. 158–165, 2013.
R. O. Mahmoud, M. M. Selim, and O. A. Muhi, “Fusion time reduction of a feature level based multimodal biometric authentication system,” Int. J. Sociotechnology Knowl. Dev., vol. 12, no. 1, pp. 67–83, 2020, doi: 10.4018/IJSKD.2020010104.
E. S. Nil and A. Chilambuchelvan, “Multimodal biometric authentication algorithm at score level fusion using hybrid optimization,” Wirel. Commun. Technol., vol. 2, no. 1, pp. 1–12, 2018, doi: 10.18063/wct.v2i1.415.
S. Soviany, V. Sandulescu, S. Puşcoci, C. Soviany, and M. Jurian, “An optimized biometric system with intraand inter-modal feature-level fusion,” Proc. 9th Int. Conf. Electron. Comput. Artif. Intell. ECAI 2017, vol. 2017-Janua, pp. 1–8, 2017, doi: 10.1109/ECAI.2017.08166466.
A. Saha, “An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition,” 2019 Int. Conf. Electr. Comput. Commun. Eng., pp. 1–5, 2019.
M. Haghighat, M. Abdel-Mottaleb, and W. Alhalabi, “Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 9, pp. 1984–1996, 2016, doi: 10.1109/TIFS.2016.2569061.
C. Kant, “Analysis of Different Fusion and Normalization Techniques,” Int. J. Adv. Res. Comput. Commun. Eng. ISO, vol. 3297, pp. 363–368, 2007, doi: 10.17148/IJARCCE.2018.7369.
P. Sai Shreyashi, R. Kalra, M. Gayathri,
and C. Malathy, “A Study on Multimodal Approach of Face and Iris Modalities in a Biometric System,” Lect. Notes Networks Syst., vol. 176 LNNS, no.
May, pp. 581–594, 2021, doi: 10.1007/978-981-33-4355-9_43.
S. N. Garg, R. Vig, and S. Gupta, “A Survey on Different Levels of Fusion in Multimodal Biometrics,” vol. 10, no. November, pp. 1–11, 2017, doi: 10.17485/ijst/2017/v10i44/120575.
S. Asim, A. Shah, H. M. Shabbir, S. U. Rehman, and M. Waqas, “A Comparative Study of Feature Selection Approaches: 2016-2020,” Int. J. Sci. Eng. Res., vol. 11, no. 2, p. 2020, 2020.
Z. Sadeghian, E. Akbar, H. Nematzadeh, and H. Motameni, “A Review of Feature Selection Method Based on Optimization Algorithms,” vol. 16, no. 1, pp. 57–73, 2023.
R. Mythily and W. A. Banu, “Feature selection for optimization algorithms: Literature survey,” Journal of Engineering and Applied Sciences, vol. 12, no. Specialissue1. pp. 5735–5739, 2017, doi: 10.3923/jeasci.2017.5735.5739.
L. Zhang, K. Mistry, C. P. Lim, and S. C. Neoh, “Feature selection using firefly optimization for classification and regression models,” Decis. Support Syst., vol. 106, pp. 64–85, 2018, doi: 10.1016/j.dss.2017.12.001.
M. Sharma and P. Kaur, “A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem,” Arch. Comput. Methods Eng., vol. 28, no. 3, pp. 1103–1127, 2021, doi: 10.1007/s11831-020-09412-6.
A. E. Ezugwu, M. B. Agbaje, N. Aljojo, R. Els, H. Chiroma, and M. A. Elaziz, “A comparative performance study of hybrid firefly algorithms for automatic data clustering,” 2020, doi: 10.1109/ACCESS.2020.3006173.
N. F. Johari, A. M. Zain, N. H. Mustaffa, and A. Udin, “Firefly algorithm for optimization problem,” Appl. Mech. Mater., vol. 421, no. April 2014, pp. 512–517, 2013, doi: 10.4028/www.scientific.net/AMM.421.512.
S. L. Tilahun and J. M. T. Ngnotchouye, “Firefly algorithm for discrete optimization problems: A survey,” KSCE J. Civ. Eng., vol. 21, no. 2, pp. 535–545, 2017, doi: 10.1007/s12205-017-1501-1.
A. B. Channegowda and H. N. Prakash, “Image fusion by discrete wavelet transform for multimodal biometric recognition,” IAES Int. J. Artif. Intell., vol. 11, no. 1, p. 229, 2022, doi: 10.11591/ijai.v11.i1.pp229-237.
I. Ahmia and M. Aïder, “A novel metaheuristic optimization algorithm: The monarchy metaheuristic,” Turkish J. Electr. Eng. Comput. Sci., vol. 27, no. 1, pp. 362–376, 2019, doi: 10.3906/elk-1804-56.
A. S. Joshi, O. Kulkarni, G. M. Kakandikar, and V. M. Nandedkar, “Cuckoo Search Optimization- A Review,” Mater. Today Proc., vol. 4, no. 8, pp. 7262–7269, 2017, doi: 10.1016/j.matpr.2017.07.055.
A. Johari, N.F., Zain, A. Z., Mustaffa, N. H & Udin, “Firefly Algorithm for Optimization Problem Firefly Algorithm for Optimization Problem,” Appl. Mech. Mater., vol. 421, pp. 512–517, 2013, doi: 10.4028/www.scientific.net/AMM.421.512.
A. Ritthipakdee, A. Thammano, N. Premasathian, and D. Jitkongchuen, “Firefly Mating Algorithm for Continuous Optimization Problems,” Comput. Intell. Neurosci., pp. 1–11, 2017.
N. Ali, M. A. Othman, M. N. Husain, and M. H. Misran, “A review of firefly algorithm,” ARPN J. Eng. Appl. Sci., vol. 9, no. 10, pp. 1732–1736, 2014.
P. D. Umbarka, A. J., Balande, U. T & Seth, “Performance evaluation of firefly algorithm with variation in sorting for non-linear benchmark problems,” 2017, doi: 10.1063/1.4981972.
P. Cao, “Comparative Study of Several Improved Firefly,” no. 6117411, pp. 910–914, 2016.
V. Kumar and D. Kumar, “A Systematic Review on Firefly Algorithm: Past, Present, and Future,” Arch. Comput. Methods Eng., vol. 28, no. 4, pp. 3269–3291, 2021, doi: 10.1007/s11831-020-09498-y.
N. N. H. Waqar A. Khan and S. L. T. and J. M. T. Ngnotchouye, “A Review and Comparative Study of Firefly Algorithm and Its Modified Versions,” Intech, no. tourism, p. 13, 2016, [Online]. Available: https://www.intechopen.com/books/advanced-biometric-technologies/liveness-detection-in-biometrics.
M. H. Mojarrad and P. Ayubi, “Particle swarm optimization with chaotic velocity clamping (CVC-PSO),” 2015 7th Conf. Inf. Knowl. Technol. IKT 2015, 2015, doi: 10.1109/IKT.2015.7288811.
R. Sujatha, B. Gomathi, and T. Padma, “An efficient method for high performance prediction mechanism for diabetes using enhanced Firefly algorithm and Map-Reduce,” J. Phys. Conf. Ser., vol. 1964, no. 4, 2021, doi: 10.1088/1742-6596/1964/4/042056.
Y. Zhang, X. fang Song, and D. wei Gong, “A return-cost-based binary firefly algorithm for feature selection,” Inf. Sci. (Ny)., vol. 418–419, pp. 561–574, 2017, doi: 10.1016/j.ins.2017.08.047.
J. M. Rubio, C. L. Vidal-Silva, R. Soto, E. Madariaga, F. Johnson, and L. Carter, “Applying FireFly Algorithm to solve the problem of balancing curricula,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 1, pp. 68–75, 2019, doi: 10.14569/IJACSA.2019.0100108.
H. J. Mohammed, F. Abdulsalam, A. S. Abdulla, and R. S. Ali, “Evaluation of Genetic Algorithms , Particle Swarm Optimisation , and Firefly Algorithms in Antenna Design,” no. 1, pp. 3–6, 2016.
H. C. Jiufang Zhou, Jianhui Wu, “Immune Multi-population Firefly Algorithm and Its Application in Multimodal Function Optimization,” no. 1, pp. 4–7, 2018.
A. Altherwi, “Application of the Firefly Algorithm for Optimal Production and Demand Forecasting at Selected Industrial Plant,” pp. 2451–2459, 2020, doi: 10.4236/ojbm.2020.86151.
G. Linhares, G. Costa, L. Jacobson, L. Rodrigues, P. S. Rodrigues, and G. A. Wachs-lopes, “Parallel Approach to the Firefly Algorithm,” no. Session III, 2018, doi: 10.1109/CLEI.2018.00094.
& N. G. Elahi, Atif, Mehreen Atif, Ijaz Mansoor Qureshi, “Interference Reduction in Cognitive Radio Networks Using Genetic and Firefly Algorithms,” no. 1, pp. 96–100, 2017.
Y. Huang, M. Huang, and C. Ye, “A Fusion Firefly Algorithm with Simplified Propagation for Photovoltaic MPPT under Partial Shading Conditions,” vol. 3029, no. c, 2020, doi: 10.1109/TSTE.2020.2968752.
S. Kitaronka, “Optimization effects of Firefly algorithm in Smart Grids,” no. February, 2022, doi: 10.6084/m9.figshare.19115237.
S. K. Sarangi, “A New Modified Firefly Algorithm for Function,” pp. 2944–2949, 2016.
Z. Orman, “A MODIFIED FIREFLY ALGORITHM-BASED FEATURE SELECTION METHOD AND ARTIFICIAL IMMUNE SYSTEM,” vol. 25, no. 1, pp. 269–288, 2020, doi: 10.17482/uumfd.649003.
A. Liu, P. Li, X. Deng, and L. Ren, “A sigmoid attractiveness based improved firefly algorithm and its applications in IIR filter design,” Conn. Sci., vol. 33, no. 1, pp. 1–25, 2021, doi: 10.1080/09540091.2020.1742660.
E. M. Mashhour, E. M. F. El Houby, K. T. Wassif, and A. I. Salah, “Feature selection approach based on firefly algorithm and chi-square,” Int. J. Electr. Comput. Eng., vol. 8, no. 4, pp. 2338–2350, 2018, doi: 10.11591/ijece.v8i4.pp2338-2350.
S. K. Sarangi, “Crazy Firefly Algorithm for Function Optimization,” vol. 2, no. 1, 2017.
Copyright (c) 2023 International Journal of Engineering and Computer Science
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.