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.

本论文提出了一种基于短程 FMCW 雷达的面部认证和 ODD 检测框架，利用重建式架构结合卷积块、编码器解码器和线性编码器解码器部分，构建了准确的人脸分类器和强大的 ODD 检测器，实验结果表明该方法在常见的 ODD 检测指标上优于之前的方法。