FARE: A Deep Learning-Based Framework for Radar-Based Face Recognition and Out-of-Distribution Detection
Sabri Mustafa Kahya, Boran Hamdi Sivrikaya, Muhammet Sami Yavuz, Eckehard G. Steinbach
Abstract
In this work, we propose a novel pipeline for face recognition and out-of-distribution (OOD) detection using shortrange FMCW radar. The proposed system utilizes RangeDoppler and micro Range-Doppler Images. The architecture features a primary path (PP) responsible for the classification of in-distribution (ID) faces, complemented by intermediate paths (IPs) dedicated to OOD detection. The network is trained in two stages: first, the PP is trained using triplet loss to optimize ID face classification. In the second stage, the PP is frozen, and the IPs—comprising simple linear autoen-coder networks—are trained specifically for OOD detection. Using our dataset generated with a 60 GHz FMCW radar, our method achieves an ID classification accuracy of 99.30% and an OOD detection AUROC of 96.91%.
BibTeX
@inproceedings{icassp2025_fareadeeplearnin,
title = {FARE: A Deep Learning-Based Framework for Radar-Based Face Recognition and Out-of-Distribution Detection},
author = {Sabri Mustafa Kahya and Boran Hamdi Sivrikaya and Muhammet Sami Yavuz and Eckehard G. Steinbach},
booktitle = {ICASSP 2025},
year = {2025}
}