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 [BibTeX] [Marc21]
Residual Feature Pyramid Network for Enhancement of Vascular Patterns
Type of publication: Conference paper
Citation: Kotwal_CVPR-W_2022
Publication status: Accepted
Booktitle: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Year: 2022
Month: June
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.
Keywords: Finger vein, residual CNN, vein enhancement
Projects Biometrics Center
Innosuisse CANDY
Authors Kotwal, Ketan
Marcel, S├ębastien
Added by: [UNK]
Total mark: 0
  • Kotwal_CVPR-W_2022.pdf