Remote photoplethysmography (rPPG) technique extracts blood volume pulse
(BVP) signals from subtle pixel changes in video frames. This study introduces
rFaceNet, an advanced rPPG method that enhances the extraction of facial BVP
signals with a focus on facial contours. rFaceNet integrates identity-specific
facial contour information and eliminates redundant data. It efficiently
extracts facial contours from temporally normalized frame inputs through a
Temporal Compressor Unit (TCU) and steers the model focus to relevant facial
regions by using the Cross-Task Feature Combiner (CTFC). Through elaborate
training, the quality and interpretability of facial physiological signals
extracted by rFaceNet are greatly improved compared to previous methods.
Moreover, our novel approach demonstrates superior performance than SOTA
methods in various heart rate estimation benchmarks.

介绍了一种名为 rFaceNet 的高级 rPPG 方法，通过整合特定身份的脸部轮廓信息和消除冗余数据，通过时间压缩单元（TCU）高效地从输入视频帧中提取面部轮廓，并使用跨任务特征组合器（CTFC）将模型关注点导向相关的面部区域，提高了 rFaceNet 提取到的面部生理信号的质量和可解释性，同时在各种心率估计基准测试中表现出卓越的性能。