{
  "id": "1701.08289",
  "version": "v1",
  "published": "2017-01-28T13:33:24.000Z",
  "updated": "2017-01-28T13:33:24.000Z",
  "title": "Face Detection using Deep Learning: An Improved Faster RCNN Approach",
  "authors": [
    "Xudong Sun",
    "Pengcheng Wu",
    "Steven C. H. Hoi"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "In this report, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detetion benchmark evaluation. In particular, we improve the state-of-the-art faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pretraining, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance, making it the best model in terms of ROC curves among all the published methods on the FDDB benchmark.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-01-28T13:33:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "face detection",
      "faster rcnn approach",
      "deep learning",
      "fddb face detetion benchmark evaluation",
      "well-known fddb face detetion benchmark"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}