Vggface2: A dataset for recognising faces across pose and age
Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew

VGG-Face2 (14 files)
README.md 1.03kB
README.txt 1.03kB
data/test_list.txt 3.39MB
data/train_list.txt 62.84MB
data/vggface2_test.tar.gz 2.03GB
data/vggface2_train.tar.gz 37.91GB
meta/bb_landmark.tar.gz 178.41MB
meta/class_overlap_vgg1_2.txt 0.97kB
meta/identity_meta.csv 335.00kB
meta/test_agetemp_imglist.txt 40.00kB
meta/test_list.txt 3.39MB
meta/test_posetemp_imglist.txt 220.80kB
meta/train_list.txt 62.84MB
samples_0.png 989.43kB
Type: Dataset
Tags: image, Face, Face Verification, In the Wild, Vision

Bibtex:
@inproceedings{cao2018vggface2,
title= {Vggface2: A dataset for recognising faces across pose and age},
author= {Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M and Zisserman, Andrew},
booktitle= {2018 13th IEEE international conference on automatic face \& gesture recognition (FG 2018)},
pages= {67--74},
year= {2018},
organization= {IEEE},
abstract= {In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimise the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on the IJB-A and IJB-B face recognition benchmarks, exceeding the previous state-of-the-art by a large margin. The dataset and models are publicly available.},
keywords= {Image, Vision, Face, Face Verification, In the Wild},
terms= {VGG2 provides loosely cropped faces in separated files to download for training and testing. More information and links for download can be found on http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/data_infor.html. You will need to create an account to be able to download the files.

Here is some information regarding VGG2 dataset:

    Number of identities: 9131 (8631 identities for training, 500 identities for testing)

    More than 3.3 million images in the wild

    Almost 362 image samples per person

If you use this dataset:

Please make sure to pay attention to the License information for using the dataset for Commercial/Research purposes (Terms of Use) available on http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.

Please make sure to cite the paper:

Q. Cao, L. Shen, W. Xie, O. M. Parkhi, A. Zisserman, VGGFace2: A Dataset for Recognizing Face across Pose and Age. International Conference on Automatic Face and Gesture Recognition, 2018.

keywords: Vision, Image, Face, Face Verification, In the Wild},
license= {},
superseded= {},
url= {Please make sure to pay attention to the License information for using the dataset for Commercial/Research purposes (Terms of Use) available on http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/.}
}

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Average Time 2 hrs, 08 mins, 09 secs
Average Speed 5.23MB/s
Best Time 5 mins, 00 secs
Best Speed 134.17MB/s
Worst Time 20 hrs, 30 mins, 19 secs
Worst Speed 545.25kB/s
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