{
  "id": "1408.3750",
  "version": "v1",
  "published": "2014-08-16T17:11:44.000Z",
  "updated": "2014-08-16T17:11:44.000Z",
  "title": "Real-time emotion recognition for gaming using deep convolutional network features",
  "authors": [
    "Sébastien Ouellet"
  ],
  "comment": "6 pages, 8 figures, IEEE style",
  "categories": [
    "cs.CV",
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "The goal of the present study is to explore the application of deep convolutional network features to emotion recognition. Results indicate that they perform similarly to other published models at a best recognition rate of 94.4%, and do so with a single still image rather than a video stream. An implementation of an affective feedback game is also described, where a classifier using these features tracks the facial expressions of a player in real-time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-08-16T17:11:44.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep convolutional network features",
      "real-time emotion recognition",
      "best recognition rate",
      "video stream",
      "affective feedback game"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1408.3750O"
    }
  }
}