This study proposes a novel approach for real-time facial expression recognition utilizing short-range Frequency-Modulated Continuous-Wave (FMCW) radar equipped with one transmit (Tx), and three receive (Rx) antennas. The system leverages four distinct modalities simultaneously: Range-Doppler images (RDIs), micro range-Doppler Images (micro-RDIs), range azimuth images (RAIs), and range elevation images (REIs). Our innovative architecture integrates feature extractor blocks, intermediate feature extractor blocks, and a ResNet block to accurately classify facial expressions into smile, anger, neutral, and no-face classes. Our model achieves an average classification accuracy of 98.91% on the dataset collected using a 60 GHz short-range FMCW radar. The proposed solution operates in real-time in a person-independent manner, which shows the potential use of low-cost FMCW radars for effective facial expression recognition in various applications.

翻译：本研究提出了一种利用配备一个发射天线和三个接收天线的短距调频连续波雷达进行实时面部表情识别的新方法。该系统同时利用四种不同的模态：距离-多普勒图像、微距离-多普勒图像、距离-方位图像和距离-仰角图像。我们创新的架构集成了特征提取器模块、中间特征提取器模块和ResNet模块，以将面部表情准确分类为微笑、愤怒、中性及无面部四种类别。我们的模型在使用60 GHz短距FMCW雷达采集的数据集上实现了98.91%的平均分类准确率。所提出的解决方案以独立于特定个体的方式实时运行，这展现了低成本FMCW雷达在各种应用中实现有效面部表情识别的潜在用途。