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

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]

You might also like...
A large-scale face dataset for face parsing, recognition, generation and editing.
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

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

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

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

Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging This repository contains an implementation

Learning To Have An Ear For Face Super-Resolution

Learning To Have An Ear For Face Super-Resolution [Project Page] This repository contains demo code of our CVPR2020 paper. Training and evaluation on

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

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation Figure 1: We estimate the 6DoF rigid transformation of a 3D face (rendered in si

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

HLA-Face: Joint High-Low Adaptation for Low Light Face Detection The official PyTorch implementation for HLA-Face: Joint High-Low Adaptation for Low L

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

SADRNet Paper link: SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction Requirements python

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

Realtime Face Anti-Spoofing Detection 🤖 Realtime Face Anti Spoofing Detection with Face Detector to detect real and fake faces Please star this repo

Releases(1.0)
Owner
Martin Knoche
PhD @ Technische Universität München
Martin Knoche
Lightweight, Python library for fast and reproducible experimentation :microscope:

Steppy What is Steppy? Steppy is a lightweight, open-source, Python 3 library for fast and reproducible experimentation. Steppy lets data scientist fo

minerva.ml 134 Jul 10, 2022
This repository contains the code for EMNLP-2021 paper "Word-Level Coreference Resolution"

Word-Level Coreference Resolution This is a repository with the code to reproduce the experiments described in the paper of the same name, which was a

79 Dec 27, 2022
The code repository for "PyCIL: A Python Toolbox for Class-Incremental Learning" in PyTorch.

PyCIL: A Python Toolbox for Class-Incremental Learning Introduction • Methods Reproduced • Reproduced Results • How To Use • License • Acknowledgement

Fu-Yun Wang 258 Dec 31, 2022
Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

Pytorch Implementation of Deep Orthogonal Fusion of Local and Global Features (DOLG) This is the unofficial PyTorch Implementation of "DOLG: Single-St

DK 96 Jan 06, 2023
Fermi Problems: A New Reasoning Challenge for AI

Fermi Problems: A New Reasoning Challenge for AI Fermi Problems are questions whose answer is a number that can only be reasonably estimated as a prec

AI2 15 May 28, 2022
Betafold - AlphaFold with tunings

BetaFold We (hegelab.org) craeted this standalone AlphaFold (AlphaFold-Multimer,

2 Aug 11, 2022
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images Histological Image Segmentation This

Saad Wazir 11 Dec 16, 2022
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
Single-Stage 6D Object Pose Estimation, CVPR 2020

Overview This repository contains the code for the paper Single-Stage 6D Object Pose Estimation. Yinlin Hu, Pascal Fua, Wei Wang and Mathieu Salzmann.

CVLAB @ EPFL 89 Dec 26, 2022
Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning

CSRL Implementation of CSRL from the AAAI2022 paper: Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learning Python: 3

4 Apr 14, 2022
This repo contains the pytorch implementation for Dynamic Concept Learner (accepted by ICLR 2021).

DCL-PyTorch Pytorch implementation for the Dynamic Concept Learner (DCL). More details can be found at the project page. Framework Grounding Physical

Zhenfang Chen 31 Jan 06, 2023
Code for the paper "Implicit Representations of Meaning in Neural Language Models"

Implicit Representations of Meaning in Neural Language Models Preliminaries Create and set up a conda environment as follows: conda create -n state-pr

Belinda Li 39 Nov 03, 2022
The implementation of 'Image synthesis via semantic composition'.

Image synthesis via semantic synthesis [Project Page] by Yi Wang, Lu Qi, Ying-Cong Chen, Xiangyu Zhang, Jiaya Jia. Introduction This repository gives

DV Lab 71 Jan 06, 2023
A scikit-learn-compatible module for estimating prediction intervals.

MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourit

588 Jan 04, 2023
In Search of Probeable Generalization Measures

In Search of Probeable Generalization Measures Exciting News! In Search of Probeable Generalization Measures has been accepted to the International Co

Mahdi S. Hosseini 6 Sep 11, 2022
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021) The implementation of Reducing Infromation Bottleneck for W

Jungbeom Lee 81 Dec 16, 2022
[ ICCV 2021 Oral ] Our method can estimate camera poses and neural radiance fields jointly when the cameras are initialized at random poses in complex scenarios (outside-in scenes, even with less texture or intense noise )

GNeRF This repository contains official code for the ICCV 2021 paper: GNeRF: GAN-based Neural Radiance Field without Posed Camera. This implementation

Quan Meng 191 Dec 26, 2022
Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch

Rotary Embeddings - Pytorch A standalone library for adding rotary embeddings to transformers in Pytorch, following its success as relative positional

Phil Wang 110 Dec 30, 2022
Server files for UltimateLabeling

UltimateLabeling server files Server files for UltimateLabeling. git clone https://github.com/alexandre01/UltimateLabeling_server.git cd UltimateLabel

Alexandre Carlier 4 Oct 10, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022