Abstract
In a finger vein authentication system, the image of a finger acquired for recognition always suffers from noises due to imperfect acquisition device, signal distortion, and variability of individual physical appearance over time. To improve the system performance, we propose a multi-instances finger vein recognition using feature level fusion. Local Hybrid Binary Gradient Contour (LHBGC) is proposed as the finger texture descriptor and SVM is used for classification. Experiments are conducted using the Shandong finger vein database (SDUMLA-HMT) and also the University Sains Malaysia finger vein database (FV-USM). Experimental results show a significant increase in performance accuracy when more than one fingers are combined, with an EER as low as 0.0038%.
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