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。

FOOD：基于短程 FMCW 雷达的面部识别和离群检测

FOOD: Facial Authentication and Out-of-Distribution Detection with  Short-Range FMCW Radar

Owing to the advances in image processing technology and large-scale
datasets, companies have implemented facial authentication processes, thereby
stimulating increased focus on face anti-spoofing (FAS) against realistic
presentation attacks. Recently, various attempts have been made to improve face
recognition performance using both global and local learning on face images;
however, to the best of our knowledge, this is the first study to investigate
whether the robustness of FAS against domain shifts is improved by considering
global information and local cues in face images captured using self-attention
and convolutional layers. This study proposes a convolutional vision
transformer-based framework that achieves robust performance for various unseen
domain data. Our model resulted in 7.3%$p$ and 12.9%$p$ increases in FAS
performance compared to models using only a convolutional neural network or
vision transformer, respectively. It also shows the highest average rank in
sub-protocols of cross-dataset setting over the other nine benchmark models for
domain generalization.

基于卷积视觉变换器的框架通过全局信息和面部图像中的局部线索来改善人脸识别性能和抵抗领域转变的作用，在面部反欺诈和领域泛化等领域取得了显著的提升。