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arXiv · 2306.17200

Residual Feature Pyramid Network for Enhancement of Vascular Patterns

Ketan Kotwal·Sebastien Marcel
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Abstract

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

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Ketan Kotwal, Sebastien Marcel. 2023-06-29. Residual Feature Pyramid Network for Enhancement of Vascular Patterns. https://doi.org/10.1109/cvprw56347.2022.00165

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