Machine Learning based Automated Multimodal Biometric Recognition for Person Identification
Keywords:Biometric verification; Person identification; Deep learning; Machine learning; Multimodal biometrics
Multi-modal biometric system combines feature knowledge indifferent traits to conquer the deficienciesof unimodal systems. But, over the last few years, the conventional biometric system focuses on the usage of handcrafted feature for human recognition. Feature extraction is certainly a crucial phase for the performance of this approach, because of the complexityofmounting consistent features to handle variations in the provided image. This article introduces an Optimal Machine Learning-based Automated Multimodal Biometrics Recognition for Person Identification (OML-AMBRPI) technique. The presented OML-AMBRPI technique makes use of three different biometrics namely iris, fingerprint, and microarray images for person recognition. For feature extraction, the OML-AMBRPI technique uses eXtendedCenter-Symmetric Local Binary Pattern (XCS-LBP) and Gabor feature extraction. Next, feature fusion process is carried out by the decision level fusion technique. At last, the multi-objective grey wolf optimizer (MOGWO) with feed forward neural network (FFNN) for the recognition process. The experimental outcome analysis of the OML-AMBRPI system demonstrates the significant performance of the OML-AMBRPI technique over other models.
AhilaPriyadharshini, R., Arivazhagan, S. and Arun, M., 2021. A deep learning approach for person identification using ear biometrics. Applied intelligence, 51(4), pp.2161-2172.
Abdeldayem, S.S. and Bourlai, T., 2019. A novel approach for ECG-based human identification using spectral correlation and deep learning. IEEE Transactions on Biometrics, Behavior, and Identity Science, 2(1), pp.1-14.
Szymkowski, M., Jasiński, P. and Saeed, K., 2021. Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform. Innovations in Systems and Software Engineering, 17(3), pp.309-317.
Patro, K.K., Jaya Prakash, A., Jayamanmadha Rao, M. and Rajesh Kumar, P., 2020. An efficient optimized feature selection with machine learning approach for ECG biometric recognition. IETE Journal of Research, pp.1-12.
Shams, T.B., Hossain, M.S., Mahmud, M.F., Tehjib, M.S., Hossain, Z. and Pramanik, M.I., 2022. EEG-based Biometric Authentication Using Machine Learning: A Comprehensive Survey. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 20(2), pp.225-241.
Benouis, M., Mostefai, L., Costen, N. and Regouid, M., 2021. ECG based biometric identification using one-dimensional local difference pattern. Biomedical Signal Processing and Control, 64, p.102226.
Ahmadi, N., Nilashi, M., Samad, S., Rashid, T.A. and Ahmadi, H., 2019. An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120, p.105701.
Mekruksavanich, S. and Jitpattanakul, A., 2021. Biometric user identification based on human activity recognition using wearable sensors: An experiment using deep learning models. Electronics, 10(3), p.308.
Hossain, S., Sultana Mitu, S., Afrin, S. and Akhter, S., 2021. A Real-time Machine Learning-Based Person Recognition System With Ear Biometrics. International Journal Of Computing and Digital System.
Patro, K.K., Reddi, S.P.R., Khalelulla, S.K., Rajesh Kumar, P. and Shankar, K., 2020. ECG data optimization for biometric human recognition using statistical distributed machine learning algorithm. The Journal of Supercomputing, 76(2), pp.858-875.
Sarangi, P.P., Nayak, D.R., Panda, M. and Majhi, B., 2022. A feature-level fusion based improved multimodal biometric recognition system using ear and profile face. Journal of Ambient Intelligence and Humanized Computing, 13(4), pp.1867-1898.
Al Alkeem, E., Yeun, C.Y., Yun, J., Yoo, P.D., Chae, M., Rahman, A. and Asyhari, A.T., 2021. Robust deep identification using ECG and multimodal biometrics for industrial internet of things. Ad Hoc Networks, 121, p.102581.
Gona, A. and Subramoniam, M., 2022. Convolutional neural network with improved feature ranking for robust multi-modal biometric system. Computers and Electrical Engineering, 101, p.108096.
Vijay, M. and Indumathi, G., 2021. Deep belief network-based hybrid model for multimodal biometric system for futuristic security applications. Journal of Information Security and Applications, 58, p.102707.
Mehraj, H. and Mir, A.H., 2021. Robust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features. Wireless Personal Communications, pp.1-22.
Matos, C.E.F., Souza, J.C., Diniz, J.O.B., Junior, G.B., de Paiva, A.C., de Almeida, J.D.S., da Rocha, S.V. and Silva, A.C., 2019. Diagnosis of breast tissue in mammography images based local feature descriptors. Multimedia Tools and Applications, 78(10), pp.12961-12986.
Mohamed, T.A. and Mustafa, M.K., 2022. Adaptive trainer for multi-layer perceptron using artificial gorilla troops optimizer algorithm. International Journal of Nonlinear Analysis and Applications.
Mazraeh, N.B., Daneshvar, A. and Roodposhti, F.R., 2022. Stock Portfolio Optimization Using a Combined Approach of Multi Objective Grey Wolf Optimizer and Machine Learning Preselection Methods. Computational Intelligence and Neuroscience, 2022.
How to Cite
Copyright (c) 2022 Venkata Ramana N., Dr. S. Anu H. Nair, Dr. K. P. Sanal Kumar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.