Papers › S³FD: Single Shot Scale-invariant Face Detector
S³FD: Single Shot Scale-invariant Face Detector
Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S³FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.
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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 Detection | FDDB | S3FD | AP | 0.983 | #5 of 11 | Archive leaderboard | report |
| Face Detection | PASCAL Face | S3FD | AP | 0.9849 | #2 of 6 | Archive leaderboard | report |
| Face Detection | WIDER Face (Easy) | S3FD(F+S+M) | AP | 0.937 | #21 of 27 | Archive leaderboard | report |
| Face Detection | WIDER Face (Hard) | S3FD(F+S+M) | AP | 0.852 | #20 of 40 | Archive leaderboard | report |
| Face Detection | WIDER Face (Medium) | S3FD(F+S+M) | AP | 0.924 | #21 of 37 | 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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