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

Related tags

Deep Learningdatabaserecognitionprotocolevaluationverificationdatasetfacefacerecognitionlfw
Overview

Cross-Quality Labeled Faces in the Wild (XQLFW)

Code style: black Downloads License Last Commit

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

đź“‚ Database and Evaluation Protocol

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

đź’» 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!

đź“– 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 Repository https://martlgap.github.io/xqlfw
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