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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 Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.

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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