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• W2963307811 abstract "This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> FD), 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." @default.
• W2963307811 created "2019-07-30" @default.
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• W2963307811 date "2017-10-01" @default.
• W2963307811 modified "2023-10-04" @default.
• W2963307811 title "S^3FD: Single Shot Scale-Invariant Face Detector" @default.
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• W2963307811 doi "https://doi.org/10.1109/iccv.2017.30" @default.
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