Combining Multiple Biometric Traits Using Asymmetric Aggregation Operators for Improved Person Recognition
| Autor: | Zahid Akhtar, Lahcene Ziet, Khan Muhammad, Mohamed Cheniti, Kamran Siddique, Abderrahmane Herbadji, Noubeil Guermat |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Physics and Astronomy (miscellaneous)
Biometrics Computer science General Mathematics Smart device 0211 other engineering and technologies asymmetric aggregaion operators 02 engineering and technology law.invention law multibiometric 0202 electrical engineering electronic engineering information engineering Computer Science (miscellaneous) Mobile database verficaion rate 021110 strategic defence & security studies Authentication business.industry lcsh:Mathematics Fingerprint (computing) Pattern recognition lcsh:QA1-939 Chemistry (miscellaneous) Face (geometry) person recognition Benchmark (computing) NIST 020201 artificial intelligence & image processing matching score fusion Artificial intelligence business |
| Zdroj: | Symmetry, Vol 12, Iss 3, p 444 (2020) Symmetry Volume 12 Issue 3 |
| ISSN: | 2073-8994 |
| Popis: | Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. Biometrics is nowadays widely being used in several daily applications ranging from smart device user authentication to border crossing. A system that uses a single source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal or unibiometrics system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multimodal or multibiometrics system. Multibiometrics systems can alleviate the error rates and some inherent weaknesses of unibiometrics systems. Therefore, we present, in this study, a novel score level fusion-based scheme for multibiometric user recognition system. The proposed framework is hinged on Asymmetric Aggregation Operators (Asym-AOs). In particular, Asym-AOs are estimated via the generator functions of triangular norms (t-norms). The extensive set of experiments using seven publicly available benchmark databases, namely, National Institute of Standards and Technology (NIST)-Face, NIST-Multimodal, IIT Delhi Palmprint V1, IIT Delhi Ear, Hong Kong PolyU Contactless Hand Dorsal Images, Mobile Biometry (MOBIO) face, and Visible light mobile Ocular Biometric (VISOB) iPhone Day Light Ocular Mobile databases have been reported to show efficacy of the proposed scheme. The experimental results demonstrate that Asym-AOs based score fusion schemes not only are able to increase authentication rates compared to existing score level fusion methods (e.g., min, max, t-norms, symmetric-sum) but also is computationally fast. |
| Databáze: | OpenAIRE |
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