Our dataset (link)
FaceScrub dataset (by downloading this dataset you agree to the conditions of the creators of the data)
You may train with any set except for FaceScrub, MegaFace, and FGNET
Some systems are trained on millions of people, and others on several thousands. One of our goals is to compare face recognition algorithms independent of the training data. Thus please specify # of training photos and # of unique people you used for training. The results will be tiered accordingly, e.g., if you trained on 1K photos you won’t compete with groups that were trained on 1M photos.
Linux Development Kit (.zip) (.tar.gz) — 16 MB
Frequently Asked Questions
What should we do if we cannot detect a face in some photos?
- If you cannot detect a face in a photo then you should use our landmarks provided in the json files.
- Landmarks meaning: landmark 0 is center of the right eye, 1-center of the left eye, 2-tip of the nose.
- In case one or more of the landmarks is missing it means that the point is occluded in the photo.
Why are there fewer FaceScrub feature files in your features than in the whole set?
- We use a subset of FaceScrub for initial tests (to speed up the testing) but will use the full FaceScrub for additional tests, please compute features for the full set.
Do we need to submit our features for the full Megaface and Facescrub?
- Yes, please submit a link to all the megaface features and all facescrub features.
Please cite the paper if you use our code, results, or dataset in a publication (link)