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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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