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

Related tags

Deep Learning database recognition protocol evaluation verification dataset face facerecognition lfw
Overview

Cross-Quality Labeled Faces in the Wild (XQLFW)

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

📂 Database and Evaluation Protocol

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

💻 Code

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

🥣 Requirements

Python 3.8

🚀 How to use

  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!

📖 Cite

If you use our code please consider citing:

@misc{knoche2021crossquality,
  title={Cross-Quality LFW: A Database for Analyzing
    Cross-Resolution Image Face Recognition in Unconstrained Environments},
  author={Martin Knoche and Stefan Hörmann and Gerhard Rigoll},
  year={2021},
  eprint={2108.10290},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

and mabybe 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}
}

✉️ Contact

For any inquiries, please open an issue on GitHub or send an E-Mail to: [email protected]

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Releases(1.0)
Owner
Martin Knoche
PhD @ Technische Universität München
Martin Knoche
GitHub https://martlgap.github.io/xqlfw
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