Aleem, S., Yang, P. orcid.org/0000-0002-8553-7127, Masood, S. et al. (2 more authors) (2020) An accurate multi-modal biometric identification system for person identification via fusion of face and finger print. World Wide Web, 23 (2). pp. 1299-1317. ISSN 1386-145X
Abstract
Internet of things (IoT) have entirely revolutionized the industry. However, the cyber-security of IoT enabled cyber-physical systems is still one of the main challenges. The success of cyber-physical system is highly reliant on its capability to withstand cyberattacks. Biometric identification is the key factor responsible for the provision of secure cyber-physical system. The conventional unimodal biometric systems do not have the potential to provide the required level of security for cyber-physical system. The unimodal biometric systems are affected by a variety of issues like noisy sensor data, non-universality, susceptibility to forgery and lack of invariant representation. To overcome these issues and to provide higher-security enabled cyber-physical systems, the combination of different biometric modalities is required. To ensure a secure cyber-physical system, a novel multi-modal biometric system based on face and finger print is proposed in this work. Finger print matching is performed using alignment-based elastic algorithm. For the improved facial feature extraction, extended local binary patterns (ELBP) are used. For the effective dimensionality reduction of extracted ELBP feature space, local non-negative matrix factorization is used. Score level fusion is performed for the fusion. Experimental evaluation is done on FVC 2000 DB1, FVC 2000 DB2, ORL (AT&T) and YALE databases. The proposed method achieved a high recognition accuracy of 99.59%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Springer Nature. |
Keywords: | Face recognition; Finger print recognition; Cyber-physical systems; Extended local binary patterns; Local non-matrix factorization |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Sep 2019 14:02 |
Last Modified: | 16 Dec 2021 10:09 |
Status: | Published |
Publisher: | Springer Nature |
Refereed: | Yes |
Identification Number: | 10.1007/s11280-019-00698-6 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151227 |