Test face recognition at the million scale. Performance with 1 million. Participate in Challenge 1. Participate in Challenge 2. Mean photos / person (3 min, 2469 max). Explore the Training Set.
http://megaface.cs.washington.edu/
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MegaFace | megaface.cs.washington.edu Reviews
https://megaface.cs.washington.edu
Test face recognition at the million scale. Performance with 1 million. Participate in Challenge 1. Participate in Challenge 2. Mean photos / person (3 min, 2469 max). Explore the Training Set.
megaface.cs.washington.edu
MegaFace - Training Set Visualization
http://megaface.cs.washington.edu/dataset/training_visualization.html
Visualization of Training Set. Our data set contains 672,057. Identities and 4,753,520. Photos. A distribution of photos per identity can be found to the right. The minimum photos per identity is 3. While the maximum is 2,469. The mean photos per identity is 7. More statistics (such as purity estimates, gender, and age detections) can be found in our publications [coming soon].
MegaFace - Challenge 1
http://megaface.cs.washington.edu/participate/challenge.html
Challenge 1: Unrestricted recognition with varying number of distractors. Fill out this form. To gain access to dataset. You will receive access information soon after if approved. View readme.txt included in development kit. Please read the FAQ below. Download MegaFace and FaceScrub datasets and development kit. Run your algorithm to produce features for both datasets. Run our experiment script with 10, 100, 1000, 10000, 100000, 1000000 distractors. Our dataset ( link. FaceScrub cropped json zip. Open s...
MegaFace - Challenge 2
http://megaface.cs.washington.edu/participate/challenge2.html
Challenge 2: Training on 672K identities. Fill out this form. To gain access to dataset. You will receive access information soon after if approved. View readme.txt included in development kit. It explains how to test your algorithm after training. Please read the FAQ below. Download our training set containing 672K identities and train your algorithm on the data ( link. Download MegaFace disjoint distractors ( link. FaceScrub ( .tgz. FGNet ( .tar.gz. And development kit ( .zip. FaceScrub ( .tgz. FGNet (...
MegaFace - Dataset Visualization
http://megaface.cs.washington.edu/dataset/visualization.html
The dataset has good distribution of locations, most of the photos were captured by DSLR cameras, tags include words from ’instagram’ to ’wedding’ which suggests a range of photos from selfies to high quality portraits (large amount of the photos came with a tag ’2013’ since the dataset is comprised of recently uploaded photos). Devices used for Photos. Some of the faces in our dataset were incorrectly identified to be faces.
MegaFace - Results
http://megaface.cs.washington.edu/results
Face recognition and verification performance under up to 1 million distractors. Performance is measured using probe and gallery images from FaceScrub, a labeled data set. FG-Net is also used to further stress the age invariance properties of algorithms. Interested to examine the results further? Download the JSON results from Challenge 1 here. Download the JSON results from Challenge 2 here.
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Test face recognition at the million scale. Performance with 1 million. Participate in Challenge 1. Participate in Challenge 2. Mean photos / person (3 min, 2469 max). Explore the Training Set.
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