Feature vector extraction and optimisation for multimodal biometrics employing face, ear and gait utilising artificial neural networks
Abstract
Cloud computing is the rapidly growing model for providing resources to users over internet. Multimodal biometrics is an upcoming research area to explore for improving the security of cloud. In this work, a novel multimodal biometric fusion system using three different biometric modalities including face, ear, and gait, based on speed-up-robust-feature (SURF) descriptor along with genetic algorithm (GA) is anticipated. Artificial neural network (ANN) is utilised as a classifier for each biometric modality. Our novel approach has been effectively tested by means of dissimilar images analogous to subjects from three databases namely AMI Ear Database, Georgia Tech Face Database and CASIA Gait Database. Before going for the fusion, the SURF features are optimised using GA and cross validated using ANN. It is observed that, the amalgamation of face, ear and gait gives better performance in terms of accuracy, precision, recall and Fmeasure.
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There is an increasing demand for hybrid approaches composed of both gait and complementary biometric characteristics, such as face and ear.
“as well as hybrid approaches composed of both gait and complementary biometric characteristics, such as face and ear [64].”
From Gait Recognition Based on Deep Learning: A Survey (2023) · cites this paper as [64]
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Gait Recognition Based on Deep Learning: A Survey
Filipi Gonçalves dos Santos, C., Oliveira, D. D. S., A. Passos, L., Gonçalves Pires, R., Felipe Silva Santos, D., Pascotti Valem, L., P. Moreira, T., Cleison S. Santana, M., Roder, M., Paulo Papa, J., et al. (2023) · ACM Computing Surveys · doi:10.1145/3490235 · Cited by 108 · see citing works
Khan, A. A., Shaikh, A. A., Shaikh, Z. A., Laghari, A. A., & Karim, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12398-x · Cited by 50 · see citing works
A Survey of Biometric Recognition Using Deep Learning
Mehraj, H., & Mir, A. H. (2020) · EAI Endorsed Transactions on Energy Web · doi:10.4108/eai.27-10-2020.166775 · Cited by 14 · see citing works
Self-citationVensila, C., & Wesley, A. B. (2023) · The Visual Computer · doi:10.1007/s00371-023-02856-4 · Cited by 13 · see citing works
A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion
Mehraj, H., & Mir, A. H. (2021) · Herald of the Russian Academy of Sciences · doi:10.1134/s1019331621020039 · Cited by 12 · see citing works
Self-citationRobust Multimodal Biometric System Based on Feature Level Fusion of Optimiseddeepnet Features
Mehraj, H., & Mir, A. H. (2021) · Wireless Personal Communications · doi:10.1007/s11277-021-09075-x · Cited by 9 · see citing works
Self-citationGait recognition methods in the task of biometric human identification
Purish, S. V., & Lobachev, M. V. (2023) · Herald of Advanced Information Technology · doi:10.15276/hait.06.2023.1 · Cited by 8 · see citing works
A Deep Multialgorithm Facial Biometric System
Mehraj, H., & Mir, A. H. (2025) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825700579
Self-citation
- Authors
- Mehraj, H., & Mir, A. H.
- Affiliations
- National Institute of Technology Srinagar (IN)
- Venue
- International Journal of Cloud Computing
- Publisher
- Inderscience Publishers
- Published
- 2020
- Volume
- 9
- Issue
- 2/3
- Pages
- 131
- ISSN
- 2043-9989 (ISSN-L)
- 2043-9997
- OpenAlex
- W4231078815
- Cited by
- 8
- References
- 0
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 2 · 2021: 2 · 2020: 1
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Cloud Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- multimodal biometrics, biometric fusion, face recognition, ear recognition, gait recognition, SURF descriptors, feature extraction, neural network classifier
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