{
  "id": "1708.03979",
  "version": "v1",
  "published": "2017-08-14T01:12:24.000Z",
  "updated": "2017-08-14T01:12:24.000Z",
  "title": "SSH: Single Stage Headless Face Detector",
  "authors": [
    "Mahyar Najibi",
    "Pouya Samangouei",
    "Rama Chellappa",
    "Larry Davis"
  ],
  "comment": "International Conference on Computer Vision (ICCV) 2017",
  "categories": [
    "cs.CV"
  ],
  "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 state-of-the-art, SSH does not use an image pyramid and is 5X faster. Moreover, if an image pyramid is deployed, our light-weight network achieves state-of-the-art on all subsets of the WIDER dataset, improving the AP by 2.5%. SSH also reaches state-of-the-art results on the FDDB and Pascal-Faces datasets while using a small input size, leading to a speed of 50 frames/second on a GPU.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-08-14T01:12:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "single stage headless face detector",
      "image pyramid",
      "wider dataset",
      "classification network",
      "state-of-the-art results"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}