rFaceNet：一种通过身份特定面部轮廓增强生理信号提取的端到端网络
rFaceNet: An End-to-End Network for Enhanced Physiological Signal Extraction through Identity-Specific Facial Contours
AI总结：
rFaceNet通过整合身份特定面部轮廓并利用TCU和CTFC模块，在端到端网络中增强rPPG信号提取，显著提升心率估计性能。
摘要：
远程光电容积描记（rPPG）技术从视频帧中细微的像素变化中提取血容量脉冲（BVP）信号。本研究介绍了rFaceNet，一种先进的rPPG方法，该方法专注于面部轮廓以增强面部BVP信号的提取。rFaceNet整合了身份特定的面部轮廓信息并消除了冗余数据。它通过时间压缩单元（TCU）从时间归一化的帧输入中高效提取面部轮廓，并利用跨任务特征组合器（CTFC）将模型聚焦于相关的面部区域。通过精心训练，与先前方法相比，rFaceNet提取的面部生理信号的质量和可解释性得到了显著提高。此外，我们的新方法在各种心率估计基准测试中表现出优于最先进（SOTA）方法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
