{
  "id": "1602.00172",
  "version": "v1",
  "published": "2016-01-30T23:59:04.000Z",
  "updated": "2016-01-30T23:59:04.000Z",
  "title": "Deep Learning For Smile Recognition",
  "authors": [
    "Patrick O. Glauner"
  ],
  "categories": [
    "cs.CV",
    "cs.LG",
    "cs.NE"
  ],
  "abstract": "Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-01-30T23:59:04.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep learning",
      "deep convolutional neural networks",
      "smile recognition test accuracy",
      "tesla k40c gpu",
      "facial expression recognition"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2016arXiv160200172G"
    }
  }
}