WIDER FACE

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on WIDER FACE

WIDER FACE:

This dataset which is a subset of WIDER dataset contains labeled face images with different poses, scales and different situations like marching or hand shaking. Separate download links are available on the dataset page for training, validation and testing with random selection of 40%, 10% and 50% of the whole data respectively. The evaluation and testing results are available for comparison on the results section of the page. You can find this information as well as the download links on http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html.

Here is some information regarding this dataset:

  • Number of images in the dataset: 32,203 images

  • Number of identities: 393,703 subjects with labeled faces

  • Image resolution: 1024×754

If you use this dataset:

Please make sure to cite the paper:

S. Yang, P. Luo, C. C. Loy, X. Tang, WIDER FACE: A Face Detection Benchmark. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

keywords: Vision, Image, Face, Face Detection, Classification, Event

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CelebA: Large-scale CelebFaces Attributes

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on CelebA: Large-scale CelebFaces Attributes

CelebA: Large-scale CelebFaces Attributes:

This dataset contains color face images with 40 attribute annotations for each image. The dataset can be used for different computer vision tasks including face detection, face attribute recognition and landmark or facial part localization. More information about the dataset and links of download can be found on the dataset page http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html or the Kaggle page https://www.kaggle.com/jessicali9530/celeba-dataset/version/2.

Here is some information regarding this dataset:

  • Number of images in the dataset: 202,599 images

  • Number of identities: 10,177 subjects

  • Image resolution: 178×218

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use).

Please make sure to cite the paper:

S. Yang, P. Luo, C. C. Loy, X. Tang, From Facial Parts Responses to Face Detection: A Deep Learning Approach. IEEE International Conference on Computer Vision (ICCV), 2015.

keywords: Vision, Image, Face, Celeb Faces, Face Recognition

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VGG & VGG2

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on VGG & VGG2

VGG & VGG2:

These two face recognition datasets contain color face images of celebrities collected from the web. The images are available with large variation of poses and ages for both datasets.

VGG

VGG has no overlap with some other popular benchmarks such as LFW. Because the images are subject to copyright and VGG does not own the images, only URLs of the images are available by VGG. More information and links for download can be found on http://www.robots.ox.ac.uk/~vgg/data/vgg_face/. Each celebrity’s name is the name of a text file containing the image URLs and corresponding face detections.

Here is some information regarding VGG dataset:

  • Number of identities: 2622

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use). The detailed Terms of Use can be found on http://www.robots.ox.ac.uk/~vgg/data/vgg_face/licence.txt).

Please make sure to cite the paper:

O. M. Parkhi, A. Vedaldi, A. Zisserman, Deep Face Recognition. British Machine Vision Conference, 2015.

VGG2

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

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AgeDB

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on AgeDB

AgeDB:

This dataset contains face images of celebrities, politicians and scientists in different ages and poses. The annotations per image include gender, age and identity of the person in the image. The age variations are from 3 to 101 years old. In the paper mentioned bellow, they have used AgeDB dataset for different experiments including age estimation, age invariant face verification and face age progression. The link for download can be found on https://ibug.doc.ic.ac.uk/resources/agedb/.

Here is some information regarding this dataset:

  • Number of images in the dataset: 12,240 images

  • Number of identities: 440 subjects

If you use this dataset:

Please make sure to use the dataset for non-commercial research purposes only (Terms of Use). The detailed Terms of Use can be found on https://ibug.doc.ic.ac.uk/resources/agedb/.

Please make sure to cite the paper:

S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, S. Zafeiriou. AgeDB: The First Manually Collected, In-the-wild Age Dataset. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR-W), 2017.

keywords: Vision, Image, Face, Age Estimation, Face Verification, Celeb Faces

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LFW: Labeled Faces in the Wild

By Amirarsalan Rajabi | November 9, 2018 | Comments Off on LFW: Labeled Faces in the Wild

LFW: Labeled Faces in the Wild:

This dataset contains labeled face images collected from the web with names of the people in the images as the labels. Some of these people have two or more number of images in the dataset. This dataset is designed for studying the problem of unconstrained face recognition and face verification. The original LFW dataset is available for download along with 3 sets of aligned images (funneled images, LFW-a, deep funneled).

Here is some information regarding this dataset:

  • Number of images in the dataset: 13,000 images (10-fold cross validation is recommended and training and test splits can be downloaded from the dataset page)

  • Number of identities: 5749

  • Image resolution: 250×250

More details and links for download can be found on the dataset page http://vis-www.cs.umass.edu/lfw/.

If you use the any of these versions of the LFW image dataset:

Please make sure to cite the paper:

G. B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, October 2007.

If you use the LFW imaged aligned by deep funneling:

Please make sure to cite the paper:

G. B. Huang, M. Matter, H. Lee, E. Learned-Miller, Learning to Align from Scratch. Advances in Neural Information Processing Systems (NIPS), 2012.

If you use the LFW imaged aligned by funneling:

Please make sure to cite the paper:

G. B. Huang, V. Jain, E. Learned-Miller, Unsupervised Joint Alignment of Complex Images. International Conference on Computer Vision (ICCV), 2007.

keywords: Vision, Image, Face, Object Detection, Segmentation, In the Wild

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