Finger Vein Comparison Redefined: Embracing Local Representations for Efficiency

Pose variations in finger vein images present a significant challenge for reliable and accurate finger vein recognition. Although various methods have been proposed to detect and correct these variations, they are often computationally expensive and fail to ensure precise alignment. We propose leveraging local finger vein representations learned by an auto-encoder to enable pose variation tolerant comparison without requiring additional alignment steps. Our proposed approach not only decreases the Equal Error Rate on the SD-HMT dataset from 7.0% to 3.52% in cross-dataset comparisons, but also reduces comparison time for one image pair from 22.1 seconds to 0.64 seconds compared to a pose correction pipeline. These results show the promise of our approach for real-word applications where efficiency and accuracy are paramount.