Awesome
Face Synthesis for Eyeglass-Robust Face Recognition
[ArXiv]
Intro
This repo releases the MeGlass dataset in original paper. MeGlass is an eyeglass dataset originaly designed for eyeglass face recognition evaluation. All the face images are selected and cleaned from MegaFace. Each identity has at least two face images with eyeglass and two face images without eyeglass. More details are presented in paper Face Synthesis for Eyeglass-Robust Face Recognition.
Name | Dataset type | Link |
---|---|---|
MeGlass_120x120.zip | Cropped | Google Drive or Baidu Yun, 335.8M |
MeGlass_ori.zip | Origin | Baidu Yun, 13.3G |
Dataset description
meta.txt
contains the eyeglass labels of images. 1
means black-eyeglass, 0
means no-eyeglass.
MeGlass_120x120.zip
consists of the cropped images of size 120x120.
MeGlass_ori.zip
contains the original face images.
test
directory contains four lists corresponding to the four protocols in paper.
Dataset | Identity | Images | Black-eyeglass | No-eyeglass |
---|---|---|---|---|
MeGlass | 1,710 | 47,917 | 14,832 | 33,085 |
Testing set | 1,710 | 6,840 | 3,420 | 3,420 |
Samples
<p align="center"> <img src="samples/samples.jpg", width="800px"> </p>Dataset usages
To build this dataset, we use eyeglass classifier, powerful face recognition model and manual labor to keep right the person identity and black eyeglass attribute. Therefore, MeGlass dataset can be used for face recognition (identification and verification), eyeglass detection, removal, generation tasks and so on.
Identity parsing rule
Take one filename 10032527@N08_identity_4@2897031059_1.jpg
for example, the string before the second @
makes one face image's identity.
The naming rule is corresponding to the original MegaFace dataset.
Acknowledgement
The 3D face model fitting is based on Xiangyu Zhu's work.
Citation
If your research benefits from MeGlass, please cite it as
@article{guo2018face,
title={Face Synthesis for Eyeglass-Robust Face Recognition},
author={Guo, Jianzhu and Zhu, Xiangyu and Lei, Zhen and Li, Stan Z},
journal={arXiv preprint arXiv:1806.01196},
year={2018}
}