{
  "id": "2008.01003",
  "version": "v1",
  "published": "2020-08-03T16:41:19.000Z",
  "updated": "2020-08-03T16:41:19.000Z",
  "title": "Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion",
  "authors": [
    "Mariana-Iuliana Georgescu",
    "Radu Tudor Ionescu"
  ],
  "comment": "Accepted at ICPR 2020",
  "categories": [
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. While previous studies show that pre-training convolutional neural networks (CNNs) on fully-visible (non-occluded) faces improves the accuracy, we propose to employ knowledge distillation to achieve further improvements. First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces. Second of all, we propose a new approach for knowledge distillation based on triplet loss. During training, the goal is to reduce the distance between an anchor embedding, produced by a student CNN that takes occluded faces as input, and a positive embedding (from the same class as the anchor), produced by a teacher CNN trained on fully-visible faces, so that it becomes smaller than the distance between the anchor and a negative embedding (from a different class than the anchor), produced by the student CNN. Third of all, we propose to combine the distilled embeddings obtained through the classic teacher-student strategy and our novel teacher-student strategy based on triplet loss into a single embedding vector. We conduct experiments on two benchmarks, FER+ and AffectNet, with two CNN architectures, VGG-f and VGG-face, showing that knowledge distillation can bring significant improvements over the state-of-the-art methods designed for occluded faces in the VR setting.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-08-03T16:41:19.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "facial expression recognition",
      "triplet loss",
      "occluded faces",
      "student cnn",
      "classic teacher-student training strategy"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}