FOOD：基于短程FMCW雷达的人脸认证与分布外检测
FOOD: Facial Authentication and Out-of-Distribution Detection with Short-Range FMCW Radar
AI总结：
本文提出FOOD框架，利用60 GHz短程FMCW雷达实现人脸认证与分布外检测，通过重建式编码器-多解码器架构取得98.07%分类准确率和98.50% AUROC，优于现有OOD检测方法。
摘要：
本文提出了一种基于短程FMCW雷达的人脸认证与分布外（OOD）检测框架。我们的流程联合估计分布内（ID）样本的正确类别，并检测OOD样本以防止其被不准确预测。我们基于重建的架构由一个主卷积块（包含一个编码器和多解码器配置）以及中间的线性编码器-解码器部分组成。这些元素共同构成了一个准确的人脸分类器和一个鲁棒的OOD检测器。对于使用60 GHz短程FMCW雷达采集的数据集，我们的网络在识别分布内人脸方面实现了98.07%的平均分类准确率。作为OOD检测器，它实现了98.50%的平均接收者操作特征曲线下面积（AUROC）和6.20%的在95%真阳性率下的平均假阳性率（FPR95）。此外，我们的大量实验表明，所提出的方法在常见OOD检测指标上优于以往的OOD检测器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
