Residual Feature Pyramid Network for Enhancement of Vascular Patterns

• URL: http://arxiv.org/abs/2306.17200v1
• Date: Thu, 29 Jun 2023 09:14:42 GMT
• Title: Residual Feature Pyramid Network for Enhancement of Vascular Patterns
• Authors: Ketan Kotwal and Sebastien Marcel
• Abstract summary: We propose a finger-vein enhancement technique, ResFPN, as a generic preprocessing method to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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