Finger Vein Presentation Attack Detection Method Using a Hybridized Gray-Level Co-Occurrence Matrix Feature with Light-Gradient Boosting Machine Model

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.