{
  "id": "2208.00544",
  "version": "v1",
  "published": "2022-07-31T23:58:35.000Z",
  "updated": "2022-07-31T23:58:35.000Z",
  "title": "Analysis of Semi-Supervised Methods for Facial Expression Recognition",
  "authors": [
    "Shuvendu Roy",
    "Ali Etemad"
  ],
  "comment": "Accepted at IEEE 10th International Conference on Affective Computing and Intelligent Interaction (ACII), 2022",
  "categories": [
    "cs.CV"
  ],
  "abstract": "Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-07-31T23:58:35.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "facial expression recognition",
      "semi-supervised learning methods",
      "labeled samples",
      "deep neural solutions",
      "training deep neural networks"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}