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Accepted author manuscript, 303 KB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 25/01/2023 |
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Host publication | 2022 IEEE 11th International Conference on Intelligent Systems, IS 2022 |
Editors | Krassimir T. Atanassov, Lyubka Doukovska, Janusz Kacprzyk, Maciej Krawczak, Jan W. Owsinski, Vassil Sgurev, Eulalia Szmidt, Slawomir Zadrozny |
Publisher | IEEE |
Pages | 1-6 |
Number of pages | 6 |
ISBN (electronic) | 9781665456562 |
ISBN (print) | 9781665492768 |
<mark>Original language</mark> | English |
Event | IEEE Intelligent Systems IS’22 - Poland, Warsaw, Poland Duration: 12/10/2022 → 14/10/2022 http://ieee-is-2022.ibspan.waw.pl/ |
Conference | IEEE Intelligent Systems IS’22 |
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Country/Territory | Poland |
City | Warsaw |
Period | 12/10/22 → 14/10/22 |
Internet address |
Conference | IEEE Intelligent Systems IS’22 |
---|---|
Country/Territory | Poland |
City | Warsaw |
Period | 12/10/22 → 14/10/22 |
Internet address |
This paper presents a multimodal biometric approach applied to all fingernails and knuckle creases of the five human fingers for identifying persons. In this paper, the proposed biometric technique consists of several phases. The method starts with the detection and localisation of the main components of the hand, defining the region of interest (ROI), segmentation, feature extraction by retraining the DenseNet201 model, measuring the similarity using different metrics, and lastly, improving the person identification performance by implementing score-level fusion. This approach presents different methods for person identification, which combine fingernails, knuckles based on the modality type, and whole hands based on different similarity metrics. This paper uses various similarity metrics to distinguish between individuals. These include the Bray-Curtis, Cosine, and Euclidean metrics. Two main score-level fusion techniques are employed: the majority voting (MV) and weighted average (WA). The experimental results are evaluated with well-known databases, the '11k Hands' and the Hong Kong Polytechnic University Contactless Hand Dorsal Images 'PolyU', show the proposed algorithm's efficiency. Using the MV on the Bray-Curtis similarity measure, the fingernail-based and the base-knuckle- based fusion obtained 100% in the identification estimation. In addition, the identification rate gained 100% in regions of hands and whole hands from the two popular datasets exceeded the performance of the state-of-the-art approaches.