用雷达实现高精度人脸识别与异常检测，精度超99%。
FARE: A Deep Learning-Based Framework for Radar-based Face Recognition and Out-of-distribution Detection
- 用雷达回波生成图像，双路径设计分别做识别人脸和检测异常
- 在60GHz雷达数据上，识别准确率99.30%，异常检测AUROC达96.91%
- 适合隐私敏感场景下的身份验证，如安防、智能门禁
本文提出一种基于短程调频连续波（FMCW）雷达的人脸识别与分布外（OOD）检测新框架。系统利用距离-多普勒图和微距离-多普勒图作为输入。网络结构包含主路径（PP）用于分布内（ID）人脸分类，以及多个中间路径（IPs）专门负责OOD检测。训练分两阶段进行：第一阶段使用三元组损失训练主路径以优化ID分类；第二阶段冻结主路径，仅训练由简单线性自编码器组成的中间路径，专注于提升OOD检测性能。基于60 GHz FMCW雷达采集的自建数据集，本方法在ID分类任务上达到99.30%的准确率，在OOD检测任务上取得96.91%的AUROC值。
原文摘要 · Abstract (English)
In this work, we propose a novel pipeline for face recognition and out-of-distribution (OOD) detection using short-range FMCW radar. The proposed system utilizes Range-Doppler 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 autoencoder 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%.
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