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

Last update: Dec 12, 2022

Overview

Cross-Quality Labeled Faces in the Wild (XQLFW)

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

📂 Database and Evaluation Protocol

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

https://martlgap.github.io/xqlfw

💻 Code

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

🥣 Requirements

🚀 How to use

Download the database and evaluation protocol here

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, ...]]

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.
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]

You might also like...

A large-scale face dataset for face parsing, recognition, generation and editing.

CelebAMask-HQ [Paper] [Demo] CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA da

1.7k Dec 26, 2022

Lightweight Face Image Quality Assessment

LightQNet This is a demo code of training and testing [LightQNet] using Tensorflow. Uncertainty Losses: IDQ loss PCNet loss Uncertainty Networks: Mobi

5 Nov 18, 2022

Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, Pattern Recognition

USDAN The implementation of Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, which is accepte

11 Nov 3, 2022

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

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

Related tags

Overview

Cross-Quality Labeled Faces in the Wild (XQLFW)

📂 Database and Evaluation Protocol

💻 Code

🥣 Requirements

🚀 How to use

📖 Cite

✉️ Contact

You might also like...

A large-scale face dataset for face parsing, recognition, generation and editing.

Lightweight Face Image Quality Assessment

Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing, Pattern Recognition

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging

Learning To Have An Ear For Face Super-Resolution

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

Code for HLA-Face: Joint High-Low Adaptation for Low Light Face Detection (CVPR21)

[TIP 2021] SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction

Realtime Face Anti Spoofing with Face Detector based on Deep Learning using Tensorflow/Keras and OpenCV

Releases(1.0)

1.0(Aug 26, 2021)

Owner

Martin Knoche

Details about the wide minima density hypothesis and metrics to compute width of a minima

PointPillars inference with TensorRT

A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python

A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

Time Delayed NN implemented in pytorch

Official PyTorch code of DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (ICCV 2021 Oral).

Code for "Modeling Indirect Illumination for Inverse Rendering", CVPR 2022

Image-to-Image Translation with Conditional Adversarial Networks (Pix2pix) implementation in keras

Аналитика доходности инвестиционного портфеля в Тинькофф брокере

code for our ECCV-2020 paper: Self-supervised Video Representation Learning by Pace Prediction

Virtual Dance Reality Stage: a feature that offers you to share a stage with another user virtually

My coursework for Machine Learning (2021 Spring) at National Taiwan University (NTU)

Banglore House Prediction Using Flask Server (Python)

Implementation of "RaScaNet: Learning Tiny Models by Raster-Scanning Image" from CVPR 2021.

HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks

[ICCV2021] Learning to Track Objects from Unlabeled Videos

Unoffical reMarkable AddOn for Firefox.

A package related to building quasi-fibration symmetries