Output bounding box, pose, landmarks, age and gender
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LFW: Labeled Faces in the Wild
http://vis-www.cs.umass.edu/lfw/
All images as gzipped tar file (173MB, md5sum a17d05bd522c52d84eca14327a23d494)
The data set contains more than 13,000 images of faces collected from the web. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector.
####Information:
- 13,233 images
- 5,749 people
- 1,680 people with two or more images
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Deep Convolutional Network Cascade for Facial Point Detection
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Large-scale CelebFaces Attributes (CelebA) Dataset
http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
10,177 number of identities,
202,599 number of face images, and
5 landmark locations, 40 binary attributes annotations per image.
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WIDER FACE: A Face Detection Benchmark
http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/
We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images.
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FDDB: Face Detection Data Set and Benchmark
http://vis-www.cs.umass.edu/fddb/index.html
This data set contains the annotations for 5171 faces in a set of 2845 images taken from the Faces in the Wild data set
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IMDB-WIKI – 500k+ face images with age and gender labels
https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
we took the list of the most popular 100,000 actors as listed on the IMDb website and (automatically) crawled from their profiles date of birth, name, gender and all images related to that person. Additionally we crawled all profile images from pages of people from Wikipedia with the same meta information.
In total we obtained 460,723 face images from 20,284 celebrities from IMDb and 62,328 from Wikipedia, thus 523,051 in total.
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Adience benchmark
Unfiltered faces for gender and age classification
https://www.openu.ac.il/home/hassner/Adience/data.html Total number of photos: 26,580 Total number of subjects: 2,284 Number of age groups / labels: 8 (0-2, 4-6, 8-13, 15-20, 25-32, 38-43, 48-53, 60-) Gender labels: Yes In the wild: Yes Subject labels: Yes
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UMDFaces Dataset
Still Images - 367,888 face annotations for 8,277 subjects.
Video Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.
We provide human curated bounding boxes for faces. We also provide the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network
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Download and unzip UMDFaces DataSet
wget https://obj.umiacs.umd.edu/umdfaces/umdfaces_images/umdfaces_batch1.tar.gz wget https://obj.umiacs.umd.edu/umdfaces/umdfaces_images/umdfaces_batch2.tar.gz wget https://obj.umiacs.umd.edu/umdfaces/umdfaces_images/umdfaces_batch3.tar.gz