FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar
<|reference_start|>FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar: 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.<|reference_end|>
@article{kahya2024food:,
    title={FOOD: Facial Authentication and Out-of-Distribution Detection with
  Short-Range FMCW Radar},
    author={Sabri Mustafa Kahya, Boran Hamdi Sivrikaya, Muhammet Sami Yavuz,
  Eckehard Steinbach},
    journal={arXiv preprint arXiv:2406.04546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04546},
    primaryClass={cs.CV cs.LG eess.SP}
}