TL;DR
SSH is a fast, single-stage, scale-invariant face detector that removes fully connected layers for efficiency and achieves state-of-the-art results on multiple datasets without relying on image pyramids.
Contribution
The paper introduces SSH, a novel single-stage, headless, scale-invariant face detector that outperforms existing methods in speed and accuracy.
Findings
Outperforms ResNet-101-based detectors on WIDER dataset
Achieves state-of-the-art results on FDDB and Pascal-Faces datasets
Runs at 50 ms per image on GPU
Abstract
We introduce the Single Stage Headless (SSH) face detector. Unlike two stage proposal-classification detectors, SSH detects faces in a single stage directly from the early convolutional layers in a classification network. SSH is headless. That is, it is able to achieve state-of-the-art results while removing the "head" of its underlying classification network -- i.e. all fully connected layers in the VGG-16 which contains a large number of parameters. Additionally, instead of relying on an image pyramid to detect faces with various scales, SSH is scale-invariant by design. We simultaneously detect faces with different scales in a single forward pass of the network, but from different layers. These properties make SSH fast and light-weight. Surprisingly, with a headless VGG-16, SSH beats the ResNet-101-based state-of-the-art on the WIDER dataset. Even though, unlike the current…
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