In this paper, we propose a method for classifying human faces using a small-sized millimeter wave radar sensor. The radar sensor transmits a frequency-modulated continuous wave signal operating in the 61 GHz band and it receives reflected signals using spatially separated receiving antenna elements. Because the shape and composition of the human face varies from person to person, the reflection characteristics of the radar signal are also distinguished from each other. Therefore, training a deep neural network (DNN) using signals received from multiple antenna elements enables classification of different human faces. With our trained DNN model, eight human faces can be classified with an accuracy of 92%. We also compare the performance of the proposed method with conventional machine learning techniques (e.g., support vector machine, tree-based methods) and confirm that our method has higher classification accuracy.

中文翻译：

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使用 61 GHz FMCW 雷达传感器的基于 DNN 的人脸分类

在本文中，我们提出了一种使用小型毫米波雷达传感器对人脸进行分类的方法。雷达传感器发射工作在 61 GHz 频段的调频连续波信号，并使用空间分离的接收天线元件接收反射信号。由于人脸的形状和构成因人而异，因此雷达信号的反射特性也有所区别。因此，使用从多个天线元件接收的信号训练深度神经网络 (DNN) 可以对不同的人脸进行分类。使用我们经过训练的 DNN 模型，可以对八张人脸进行分类，准确率为 92%。我们还将所提出的方法的性能与传统的机器学习技术（例如，支持向量机、