{
  "id": "2308.11896",
  "version": "v1",
  "published": "2023-08-23T03:43:34.000Z",
  "updated": "2023-08-23T03:43:34.000Z",
  "title": "Age Prediction From Face Images Via Contrastive Learning",
  "authors": [
    "Yeongnam Chae",
    "Poulami Raha",
    "Mijung Kim",
    "Bjorn Stenger"
  ],
  "comment": "MVA2023",
  "categories": [
    "cs.CV"
  ],
  "abstract": "This paper presents a novel approach for accurately estimating age from face images, which overcomes the challenge of collecting a large dataset of individuals with the same identity at different ages. Instead, we leverage readily available face datasets of different people at different ages and aim to extract age-related features using contrastive learning. Our method emphasizes these relevant features while suppressing identity-related features using a combination of cosine similarity and triplet margin losses. We demonstrate the effectiveness of our proposed approach by achieving state-of-the-art performance on two public datasets, FG-NET and MORPH-II.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-08-23T03:43:34.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "face images",
      "age prediction",
      "contrastive learning",
      "triplet margin losses",
      "relevant features"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}