{
  "id": "2004.11823",
  "version": "v1",
  "published": "2020-04-08T03:12:49.000Z",
  "updated": "2020-04-08T03:12:49.000Z",
  "title": "Facial Expression Recognition with Deep Learning",
  "authors": [
    "Amil Khanzada",
    "Charles Bai",
    "Ferhat Turker Celepcikay"
  ],
  "comment": "6 pages",
  "categories": [
    "cs.CV"
  ],
  "abstract": "One of the most universal ways that people communicate is through facial expressions. In this paper, we take a deep dive, implementing multiple deep learning models for facial expression recognition (FER). Our goals are twofold: we aim not only to maximize accuracy, but also to apply our results to the real-world. By leveraging numerous techniques from recent research, we demonstrate a state-of-the-art 75.8% accuracy on the FER2013 test set, outperforming all existing publications. Additionally, we showcase a mobile web app which runs our FER models on-device in real time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-04-08T03:12:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "facial expression recognition",
      "fer models on-device",
      "fer2013 test set",
      "implementing multiple deep learning models",
      "people communicate"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}