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Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model
Abstract
Presentation Attack Detection (PAD) is crucial in biometric finger vein recognition. The susceptibility of these systems to forged finger vein images is a significant challenge. Existing approaches to mitigate presentation attacks have computational complexity limitations and limited data availability. This study proposed a novel method for identifying presentation attacks in finger vein biometric systems. We have used optimal Gray-Level Co-occurrence Matrix (GLCM) features with the Light-Gradient Boosting Machine (LGBM) classification model. We use statistical texture attributes namely, energy, correlation, and contrast to extract optimal features from counterfeit and authentic finger-vein images. The study investigates cluster-pixel connectivity in finger vein images. Our approach is tested using K-fold cross-validation and compared to existing methods. Results demonstrate that Light-GBM outperforms other classifiers. The proposed classifier achieved low APCER values of 2.73% and 8.80% compared to other classifiers. The use of Light-GBM in addressing presentation attacks in finger vein biometric systems is highly significant.
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Authors (6)
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Inam Ullah
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Abdullateef O. Balogun
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Luiz Fernando Capretz
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Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Shaheed K., Szczuko P., Ullah I., Mojeed H., Balogun A. O., Capretz L. F.: Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.62036/isd.2024.54
- Sources of funding:
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- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
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