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         author = {Kotwal, Ketan and Marcel, S{\'{e}}bastien},
       keywords = {Finger vein, residual CNN, vein enhancement},
       projects = {Biometrics Center, Innosuisse CANDY},
          month = jun,
          title = {Residual Feature Pyramid Network for Enhancement of Vascular Patterns},
      booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
           year = {2022},
       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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2022/Kotwal_CVPR-W_2022.pdf}