1/2024 - 10 | View TOC | « Previous Article | Next Article » |
Biometric Identification Advances: Unimodal to Multimodal Fusion of Face, Palm, and Iris FeaturesKADHIM, O. N. , ABDULAMEER, M. H. |
View the paper record and citations in |
Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF (1,160 KB) | Citation | Downloads: 748 | Views: 668 |
Author keywords
deep learning, feature extraction, feature level fusion, multimodal biometrics identification, machine learning
References keywords
biometric(14), recognition(13), face(12), multimodal(10), fusion(10), system(7), learning(7), deep(7), vein(5), palmprint(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2024-02-29
Volume 24, Issue 1, Year 2024, On page(s): 91 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.01010
Web of Science Accession Number: 001178765900005
SCOPUS ID: 85189474643
Abstract
Due to increased information security concerns, biometric recognition technology has become more important. Unimodal biometrics still work effectively, but they struggle with noise sensitivity and spoof attack susceptibility since they rely on a single data source. This paper uses advances in deep learning and machine learning to propose new unimodal systems for the palm, face, and iris. These models use deep wavelet transform networks (WTN) for face and iris identification and deep convolutional neural networks (CNNs) for palmprint identification. In addition, we introduce a novel multimodal biometric system based on unimodal systems. We get 98.29% for face, 98.86% for palmprint, and 95.59% for iris in individual unimodal systems with Support Vector Machines (SVM). This is done by using the new property MULB dataset, which has many biometric features. The multimodal system achieves 99.88% accuracy and a 0.0186 equal error rate, underscoring the relevance of several biometric features and the superior performance of the identification system. |
References | | | Cited By «-- Click to see who has cited this paper |
[1] Y. Wang, D. Shi, and W. Zhou, "Convolutional neural network approach based on multimodal biometric system with fusion of face and finger vein features," Sensors, vol. 22, no. 16, pp. 1-15, 2022. [CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 29] [2] L. Huang and Y. Wu, "Structure-aware heatmap and boundary map regression based robust face alignment," Advances in Electrical Computer Engineering, vol. 23, no. 2, pp. 3-10, 2023. [CrossRef] [Full Text] [SCOPUS Times Cited 1] [3] R. Gad, A. El-Sayed, N. El-Fishawy, and M. Zorkany, "Multi-biometric systems: a state of the art survey and research directions," International Journal of Advanced Computer Science Applications, vol. 6, no. 6, pp. 128-138, 2015. [CrossRef] [4] M. O. Oloyede and G. P. Hancke, "Unimodal and multimodal biometric sensing systems: A review," IEEE access, vol. 4, pp. 7532-7555, 2016. [CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 113] [5] M. Singh, R. Singh, and A. Ross, "A comprehensive overview of biometric fusion," Information Fusion, vol. 52, no. 2, pp. 1-24, 2019. [CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 167] [6] H. Al-Mahafzah, T. AbuKhalil, M. Alksasbeh, and B. Alqaralleh, "Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion," International Journal of Electrical Computer Engineering, vol. 13, no. 2, pp. 1954-1963, 2023. [CrossRef] [7] M. I. Ahmad, W. L. Woo, and S. Dlay, "Non-stationary feature fusion of face and palmprint multimodal biometrics," Neurocomputing, vol. 177, pp. 49-61, 2016. [CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 51] [8] V. Timcenko and S. Gajin, "Machine learning enhanced entropy-based network anomaly detection," Advances in Electrical Computer Engineering, vol. 21, no. 4, pp. 51-60, 2021. [CrossRef] [Full Text] [SCOPUS Times Cited 5] [9] P. Wang, E. Fan, and P. Wang, "Comparative analysis of image classification algorithms based on traditional machine learning and deep learning," Pattern Recognition Letters, vol. 141, pp. 61-67, 2021. [CrossRef] [Web of Science Times Cited 297] [SCOPUS Times Cited 414] [10] B. Ammour, L. Boubchir, T. Bouden, and M. Ramdani, "Face-iris multimodal biometric identification system," Electronics, vol. 9, no. 1, pp. 1-18, 2020. [CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 63] [11] M. Chaa, N.-E. Boukezzoula, and A. Attia, "Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems," Journal of Electronic Imaging, vol. 26, no. 1, pp. 1-12, 2017. [CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 21] [12] F. M. Bachay and M. H. Abdulameer, "Hybrid deep learning model based on autoencoder and CNN for palmprint authentication," International Journal of Intelligent Engineering Systems, vol. 15, no. 3, pp. 488-499, 2022. [CrossRef] [SCOPUS Times Cited 7] [13] M. H. Ibrahem and M. H. Abdulameer, "Age invariant face recognition model based on convolution neural network (CNN)," Journal of Al-Qadisiyah for Computer Science and Mathematics vol. 1, no. 1, pp. 96-110, 2023. [CrossRef] [14] M. H. Ibrahem and M. H. Abdulameer, "Age face invariant recognition model based on VGG face based DNN and support vector classifier," International Journal on Technical and Physical Problems of Engineering vol. 15, no. 45, pp. 232-240, 2023 [15] H. N. Costin, I. Ciocoiu, T. Barbu, and C. Rotariu, "Through biometric card in Romania: person identification by face, fingerprint and voice recognition," International Journal of Biological Medical Sciences, vol. 1, no. 4, pp. 264-269, 2006. [CrossRef] [16] M. Micucci and A. Iula, "Recognition performance analysis of a multimodal biometric system based on the fusion of 3D ultrasound hand-geometry and palmprint," Sensors, vol. 23, no. 7, pp. 1-14, 2023. [CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 7] [17] A. Attia, S. Mazaa, Z. Akhtar, and Y. Chahir, "Deep learning-driven palmprint and finger knuckle pattern-based multimodal Person recognition system," Multimedia Tools Applications, vol. 81, no. 8, pp. 10961-10980, 2022. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 19] [18] B. C. Arjun and H. 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, no. 4, pp. 123-128, 2021. [CrossRef] [SCOPUS Times Cited 2] [19] N. Alay and H. H. Al-Baity, "Deep learning approach for multimodal biometric recognition system based on fusion of iris, face, and finger vein traits," Sensors, vol. 20, no. 19, pp. 1-17, 2020. [CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 98] [20] R. O. Mahmoud, M. M. Selim, and O. A. Muhi, "Fusion time reduction of a feature level based multimodal biometric authentication system," International Journal of Sociotechnology Knowledge Development, vol. 12, no. 1, pp. 67-83, 2020. [CrossRef] [SCOPUS Times Cited 21] [21] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint face detection and alignment using multitask cascaded convolutional networks," IEEE signal processing letters, vol. 23, no. 10, pp. 1499-1503, 2016. [CrossRef] [Web of Science Times Cited 1482] [SCOPUS Times Cited 4389] [22] A. Rehman, M. Harouni, M. Omidiravesh, S. M. Fati, and S. A. Bahaj, "Finger vein authentication based on wavelet scattering networks," Computers, Materials Continua, vol. 72, no. 2, 2022. [CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3] [23] J. Anden and S. Mallat, "Deep scattering spectrum," IEEE Transactions on Signal Processing, vol. 62, no. 16, pp. 4114-4128, 2014. [CrossRef] [Web of Science Times Cited 334] [SCOPUS Times Cited 403] [24] R. T. Mohammed, H. Kaur, B. Alankar, and R. Chauhan, "Recognition of human Iris for biometric identification using Daugman's method," IET Biometrics, vol. 11, no. 4, pp. 304-313, 2022. [CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 5] [25] A. Matin, F. Mahmud, S. T. Zuhori, and B. Sen, "Human iris as a biometric for identity verification," in Proc 2nd International Conference on Electrical, Computer & Telecommunication Engineering Bangladesh, 2016. [CrossRef] [SCOPUS Times Cited 15] [26] S. Almabdy and L. Elrefaei, "Deep convolutional neural network-based approaches for face recognition," applied sciences, vol. 9, no. 20, pp. 1-21, 2019. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 111] [27] H. M. L. Aung, C. Pluempitiwiriyawej, K. Hamamoto, and S. Wangsiripitak, "Multimodal biometrics recognition using a deep convolutional neural network with transfer learning in surveillance videos," computation, vol. 10, no. 127, pp. 1-15, 2022. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 10] Web of Science® Citations for all references: 2,548 TCR SCOPUS® Citations for all references: 5,954 TCR Web of Science® Average Citations per reference: 88 ACR SCOPUS® Average Citations per reference: 205 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-10-14 06:03 in 177 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.