Design and Development of an Improved Multimodal Biometric Authentication System using Machine learning Classifiers

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : R. Nanmaran, V. Velmurugan, Babushanmugham, Sivachandar Kasiviswanathan, S. Srimathi

10.14445/23488379/IJEEE-V10I5P102

How to Cite?

R. Nanmaran, V. Velmurugan, Babushanmugham, Sivachandar Kasiviswanathan, S. Srimathi, "Design and Development of an Improved Multimodal Biometric Authentication System using Machine learning Classifiers," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 5, pp. 14-22, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I5P102

Abstract:

A multi modality biometric authentication system can combine information from various modalities and provides accurate results compared to biometric systems used individually. A novel ensemble classifier-based multimodal biometric authentication system has been proposed in this work. The performance of the proposed multimodal authentication system is measured using parameters such as Accuracy, Sensitivity and Specificity and compared with the SVM classifier, Decision tree classifier when fingerprint, Iris, and Face features are used. The results of the multimodal biometric system are also compared with the biometric authentication system when fingerprint features are used and combined with Fingerprint & Iris features. The proposed ensemble classifier-based multimodal biometric authentication system provides an accuracy of 96.75%, Sensitivity of 94.74%, Specificity of 98.95%, FAR of 1.04 and FRR of 5.26. The proposed ensemble classifier outperforms SVM and decision tree classifiers regarding performance measures.

Keywords:

Authentication, SVM classifier, Decision tree classifier, Ensemble classifier.

References:

[1] D. Jagadiswary, and D. Saraswady, “Biometric Authentication using Multimodal Biometric,” Procedia Computer Science, vol. 85, pp. 109-116, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Manju Dhanraj Pawar, R. D. kokatte, and V. R. Gosavi, “An Optimize Multimodal Biometric Authentication System for Low Classification Error Rates using Face and Fingerprint,” International Conference on IoT based Control Networks and Intelligent Systems, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Sandeep Singh Sengar, U. Hariharan, and K. Rajkumar, “Multimodal Biometric Authentication System using Deep Learning Method,” 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Terence Sim et al., “Continuous Verification using Multimodal Biometrics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 687-700, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Ajay Kumar, Vivek Kanhangad, and David Zhang, “A New Framework for Adaptive Multimodal Biometrics Management,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 1, pp. 92-102, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[6] William Stallings, Cryptography and Network Security, 4th Edition, Prentice Hall, 2005.
[Google Scholar] [Publisher Link]
[7] Alfred J. Menezes, Paul C. Van Oorschot, and Scott A. Vanstone, Handbook of Applied Cryptography, CRC Press, pp. 756-799, 2001.
[Google Scholar] [Publisher Link]
[8] Samarth Bharadwaj, Mayank Vatsa and Richa Singh, “Biometric Quality: A Review of Fingerprint, Iris, and Face,” EURASIP Journal on Image and Video Processing, pp. 2014-2034, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Anil K. Jain, and Jianjiang Feng, “Latent Fingerprint Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 88-100, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Diego Marin et al., “A New Supervised Method for Blood Vessel Segmentation in Retinal Images by using Gray-Level and Moment Invariants-Based Features,” IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 146-158, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[11] J. Ayangbekun Oluwafemi, Olowookere A. Sunday, and Shoewu Oluwagbemiga, “Two Way Mobile Authentication Security Mechanisms for an Enterprise System,” SSRG International Journal of Computer Science and Engineering, vol. 1, no. 8, pp. 1-5, 2014.
[CrossRef] [Publisher Link]
[12] Maneesh Upmanyu et al., “Blind Authentication: A Secure Crypto-Biometric Verification Protocol,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 2, pp. 225-268, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ajay Kumar, and Yingbo Zhou, “Human Identification using Finger Images,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2228-2244, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[14] G. R. Blakley, “Safeguarding Cryptographic Keys,” In the Proceedings of the International Workshop on Managing Requirements Knowledge, pp. 313, 1979.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Pim Tuyls et al., “Visual Crypto Displays Enabling Secure Communications,” In: Hutter, D., Müller, G., Stephan, W., Ullmann, M. (eds) Security in Pervasive Computing. Lecture Notes in Computer Science, vol. 2802, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Zhifang Wang et al., “Multimodal Biometric System using Face-Iris Fusion Feature,” Journal of Computers, vol. 6, no. 5, pp. 931-938, 2011. [Google Scholar] [Publisher Link]
[17] V. Sandhya, and Nagaratna P. Hegde, “Implementation of Fusion of Sclera and Periocular as a Biometric Authentication System using Deep Learning,” International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 212-221, 2022.
[Google Scholar] [Publisher Link]
[18] S. Brindha et al., “A Perspective of Finger Vein Pattern Based Testifying System,” International Journal of Engineering Trends and Technology, vol. 68, no. 3, pp. 26-31, 2020. [CrossRef] [Publisher Link]
[19] Di Miao et al., “Bin-Based Classifier Fusion of Iris and Face Biometrics,” Neurocomputing, vol. 224, pp. 105-118, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Hunny Mehrotra et al., “Incremental Granular Relevance Vector Machine: A Case Study in Multimodal Biometrics,” Pattern Recognition, vol. 56, pp. 63-76, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Majid Komeili, Narges Armanfard, and Dimitrios Hatzinakos, “Liveness Detection and Automatic Template Updating using Fusion of ECG and FIngerprint,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 7, pp. 1810-1822, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Chen Xing Zhao et al., “Securing Handheld Devices and Fingerprint Readers with ECG Biometrics,” In the Proceedings of the 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, pp. 150-155, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[23] B. E. Manjunathswamy et al., “Multimodel Biometrics using ECG and Fingerprint,” International Journal of Computer Applications, pp. 777-784, 2014.
[Google Scholar]
[24] Yogendra Narain Singh, Sanjay Kumar Singh, and Phalguni Gupta, “Fusion of Electrocardiogram with Unobtrusive Biometrics: An Efficient Individual Authentication System,” Pattern Recognition Letters, vol. 33, no. 14, pp. 1932-1941, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[25] O. C. Kurban, T. Yildirim, and A. Bilgiç, “A Multi-Biometric Recognition System Based on Deep Features of Face and Gesture Energy Image,” In the Proceedings of the IEEE International Conference on INnovations in Intelligent SysTems and Applications, pp. 361-364, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Disha Lobo, C. V. Anoop, and Y. Mahesha, “Securing Fingerprint Based Biometric System,” SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 10, pp. 1-8, 2016.
[CrossRef] [Publisher Link]
[27] Alaa S. Al-Waisy et al., “A Multi-Biometric Iris Recognition System based on a Deep Learning Approach,” Pattern Analysis and Applications, vol. 21, pp. 783-802, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Veeru Talreja, Matthew C. Valenti, and Nasser M. Nasrabadi, “Multibiometric Secure System Based on Deep Learning,” In the Proceedings of the IEEE Global Conference on Signal and Information Processing, pp. 298-302, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Alessandra Lumini, and Loris Nanni, “An Improved BioHashing for Human Authentication,” Pattern Recognition, vol. 40, no. 3, pp. 1057-1065, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[30] P. Jennifer, and A. Muthu Kumaravel, “An Iris Based Authentication System by Eye Localization,” International Journal of Biotech Trends and Technology, vol. 3, no. 4, pp. 9-12, 2013.
[Google Scholar] [Publisher Link]
[31] H. Mohamed, and K. Wang, “Fingerprint Classification Based on a Q-Gaussian Multiclass Support Vector Machine,” In the Proceedings of the 2017 International Conference on Biometrics Engineering and Application, pp. 39-44, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[32] R. Nanmaran et al., “Investigating the Role of Image Fusion in Brain Tumor Classification Models Based on Machine Learning Algorithm for Personalized Medicine,” Computational and Mathematical Methods in Medicine, vol. 2022, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[33] N. Kanagathara, and R. Nanmaran, “Illustration of Potential Energy Surface from DFT Calculation Along with Fuzzy Logic Modelling for Optimization of N-Acetylglycine,” Computational and Theoretical Chemistry, vol. 1202, pp. 113301, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[34] S. Srimathi, G. Yamuna, and R. Nanmaran, “An Efficient Cancer Classification Model for CT/MRI/PET Fused Images,” Current Medical Imaging, vol. 17, no. 3, pp. 319-330, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[35] R. Nanmaran, and B. Luminasree, “Development of Wavelet Transform-Based Image Fusion Technique with Improved PSNR for CT and PET Images in Comparison with Discrete Cosine Transform-Based Image Fusion Technique,” ECS Transactions, vol. 107, no. 1, pp. 13185, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Deepak Singh, and Mohan Rao Mamdikar, “Identify a Person from Iris Pattern using GLCM Features and Machine Learning Techniques,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 9, pp. 25-29, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[37] N. G. Rahul Jayanthi, and R. Nanmaran, “Extraction of Minute Fingerprint Features using Grey Level Co-occurrence Matrix Method with Improved FAR and FRR For Fingerprint Authentication in Comparison with Principal Component Analysis,” SPAST Abstracts, vol. 1, no. 1, 2021.
[Google Scholar] [Publisher Link]
[38] R. Nanmaran, and S. Hari Priya, “Design and Development of Decorrelation Stretch Technique for Enhancing the Quality of Satellite Images with Improved MSE and UIQI in Comparison with Wiener Filter,” ECS Transactions, vol. 107, no. 1, pp. 13279, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[39] S. Srimathi, G. Yamuna, and R. Nanmaran, “Neural Networks Based Cancer Classification Model using CT-PET Fused Images,” In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences, In the Proceedings of the Communications in Computer and Information Science, vol. 1045, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[40] S. Srimathi, G. Yamuna, and R. Nanmaran, “Threshold Based Stochastic Regression Model with Gabor Filter for Segmentation and Random Forest Classification of Lung Cancer,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 4, pp. 1666-1673, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[41] R. Nanmaran, G. Thirugnanam, and P. Mangaiyarkarasi, “Medical Image Multiple Watermarking Schemes Based on Integer Wavelet Transform and Extraction using ICA,” In International Conference on Advances in Computing and Data Sciences, pp. 44-53, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[42] R. Nanmaran, and G. Thirugnanam, “Multiple Image Watermarking based on Hybrid Steerable Pyramid with DWT and Extraction using ICA,” International Journal of Research and Analytical Reviews, vol. 5, no. 4, pp. 563-568, 2018.
[Google Scholar]
[43] N. Rajendiran, T. Gurunathan, and M. Palanivel, “Wavelet Packet Transform-Based Medical Image Multiple Watermarking with Independent Component Analysis Extraction,” International Journal of Medical Engineering and Informatics, vol. 12, no. 4, pp. 322-335, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[44] Nibedita Dey et al., “Nanomaterials for Transforming Barrier Properties of Lignocellulosic Biomass Towards Potential Applications - A Review,” Fuel, vol. 316, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[45] Nibedita Dey et al., “Nanotechnology-Assisted Production of Value-Added Biopotent Energy-Yielding Products from Lignocellulosic Biomass Refinery - A Review,” Bioresource Technology PART A, vol. 344, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[46] P. Sai Preethi et al., “Advances in Bioremediation of Emerging Contaminants from Industrial Wastewater by Oxidoreductase Enzymes,” Bioresource Technology, vol. 359, 2022.
[CrossRef] [Google Scholar] [Publisher Link]