Residual Feature Pyramid Network for Enhancement of Vascular Patterns
This work addresses accuracy degradation in biometric security systems, offering an incremental improvement with cross-dataset applicability.
The paper tackled the problem of low and uneven contrast in finger vein recognition systems by proposing ResFPN, a preprocessing method that enhances vascular patterns, resulting in up to a 5% reduction in average recognition errors across datasets.
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.