用低成本雷达实时识别面部表情，准确率超98%。
FERT: Real-Time Facial Expression Recognition with Short-Range FMCW Radar
- 结合四种雷达图像模态，用改进的ResNet架构提取特征
- 在60GHz雷达数据上实现98.91%平均准确率
- 无需特定人员训练，适合隐私敏感场景
本研究提出一种基于短距离调频连续波（FMCW）雷达的实时面部表情识别新方法，采用1发3收天线配置。系统同时利用四种不同模态：距离-多普勒图（RDIs）、微距离-多普勒图（micro-RDIs）、距离方位图（RAIs）和距离俯仰图（REIs）。创新性架构融合特征提取块、中间特征提取块与ResNet块，将面部表情准确分类为微笑、愤怒、中性及无脸四类。在60 GHz短距FMCW雷达采集的数据集上，模型平均分类准确率达98.91%。所提方案实现人无关的实时运行，展现了低成本FMCW雷达在多种应用场景中进行有效面部表情识别的潜力。
原文摘要 · Abstract (English)
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.
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