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Ground-truth for the UTFVP fingervein database (UTFVP-GR)

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Artificial Intelligence and Human Interfaces (AIHI) Department of the University of Salzburg led by Andreas Uhl. Our research is focused on Visual Data Processing and associated security questions. Most of our work is currently concentrated on Biometrics, Media Forensics and Media Security, Medical Image and Video Analysis, and application oriented fundamental research in digital humanities, individualised aquaculture and sustainable wood industry.

Paper

Ehsaneddin Jalilian, Andreas Uhl, "Enhanced Segmentation-CNN based Finger-Vein Recognition by Joint Training with Automatically Generated and Manual Labels", in Proceedings of the IEEE 5th International Conference on Identity, Security and Behavior Analysis (ISBA 2019), pp. 1-8, IDRBT, January 22 - January 24

Groundtruth Database

On this page we provide the ground-truth masks for a part (388 samples) in the UTFVP fingervein database for direct use (i.e. training segmentation-based CNNs).

Note that the UTFVP-GR database only contains ground-truth not the original images. The source databases to which the ground truths apply can be found at: https://pythonhosted.org/bob.db.utfvp/

If you are using this ground-truth masks in your work, please cite the paper:

Bibtex

@inproceedings{Jalilian18b,
      author = {Ehsaneddin Jalilian and Andreas Uhl},
      title = {Enhanced Segmentation-CNN based Finger-Vein Recognition by Joint Training with Automatically Generated and Manual Labels},
      booktitle = {Proceedings of the IEEE 5th International Conference on Identity, Security and Behavior Analysis (ISBA 2019)},
      year = {2019},
      date = {January 22 - January 24},
      pages = {1--8},
      address = {IDRBT},
}

UTFVP-GR

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Description:

This package only contains the masks already extracted from the UTFVP database.

Download UTFVP-GR:

The data/code is available upon request:

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