{
  "id": "2406.04546",
  "version": "v1",
  "published": "2024-06-06T23:08:03.000Z",
  "updated": "2024-06-06T23:08:03.000Z",
  "title": "FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar",
  "authors": [
    "Sabri Mustafa Kahya",
    "Boran Hamdi Sivrikaya",
    "Muhammet Sami Yavuz",
    "Eckehard Steinbach"
  ],
  "comment": "Accepted at ICIP 2024",
  "categories": [
    "cs.CV",
    "cs.LG",
    "eess.SP"
  ],
  "abstract": "This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. Our reconstruction-based architecture consists of a main convolutional block with one encoder and multi-decoder configuration, and intermediate linear encoder-decoder parts. Together, these elements form an accurate human face classifier and a robust OOD detector. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces. As an OOD detector, it achieves an average Area Under the Receiver Operating Characteristic (AUROC) curve of 98.50% and an average False Positive Rate at 95% True Positive Rate (FPR95) of 6.20%. Also, our extensive experiments show that the proposed approach outperforms previous OOD detectors in terms of common OOD detection metrics.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-06-06T23:08:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "out-of-distribution detection",
      "ood detector",
      "short-range fmcw radar-based facial authentication",
      "intermediate linear encoder-decoder parts",
      "accurate human face classifier"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}