Papers › Deep Face Recognition
Deep Face Recognition
O. M. Parkhi, A. Vedaldi, A. Zisserman
The goal of this paper is face recognition -- from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors: (i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability of very large scale training datasets. We make two contributions: first, we show how a very large scale dataset (2.6M images, over 2.6K people) can be assembled by a combination of automation and human in the loop, and discuss the trade off between data purity and time; second, we traverse through the complexities of deep network training and face recognition to present methods and procedures to achieve comparable state of the art results on the standard LFW and YTF face benchmarks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Recognition | CASIA-WebFace+masks | VGG-Face | Accuracy | 79.65 | #6 of 6 | Archive leaderboard | report |
| Face Recognition | CelebA+masks | VGG-Face | Accuracy | 84.56 | #6 of 6 | Archive leaderboard | report |
| Face Verification | Labeled Faces in the Wild | VGG-Face | Accuracy | 98.78% | #4 of 7 | Archive leaderboard | report |
| Face Verification | YouTube Faces DB | VGG-Face | Accuracy | 97.40% | #4 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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