Here, we release the database, evaluation protocol and code for the following paper:

• Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

If you are interested in our Database and Evaluation Protocol please visit our website.

• https://martlgap.github.io/xqlfw

We provide the code to calculate the accuracy for face recognition models on the XQLFW evaluation protocol.

1. Download the database and evaluation protocol here
2. Inference the images and save the embeddings and labels to a numpy file (*.npy) according to:

   [[pair1_img1_embed, pair1_img2_embed, pair2_img1_embed, pair2_img2_embed, ...], 
   [True, True, False, ...]]

3. Run the evaluate.py code with --source_embedding argument containing the absolute path to a directory containing your embedding .npy files:

   python evaluate.py --source_embeddings="path/to/your/folder" --csv --save

   • Use the flag --csv if you want to get the results displayed in csv instead of a table.
   • Use the flag --save to save the results into the source_embedding directory.
4. See the results and enjoy!

If you use our code please consider citing:

@inproceedings{knoche2021xqlfw,
  author={Knoche, Martin and Hoermann, Stefan and Rigoll, Gerhard},
  booktitle={2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)}, 
  title={Cross-Quality LFW: A Database for Analyzing Cross- Resolution Image Face Recognition in Unconstrained Environments}, 
  year={2021},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/FG52635.2021.9666960}
}

and maybe also:

@TechReport{LFWTech,
  author={Gary B. Huang and Manu Ramesh and Tamara Berg
    and Erik Learned-Miller},
  title={Labeled Faces in the Wild: A Database for Studying
    Face Recognition in Unconstrained Environments},
  institution={University of Massachusetts, Amherst},
  year={2007},
  number={07-49},
  month={October}
}

@TechReport{LFWTechUpdate,
  author={Huang, Gary B and Learned-Miller, Erik},
  title={Labeled Faces in the Wild: Updates and New
    Reporting Procedures},
  institution={University of Massachusetts, Amherst},
  year={2014},
  number={UM-CS-2014-003},
  month={May}
}

For any inquiries, please open an issue on GitHub or send an E-Mail to: Martin.Knoche@tum.de