Leveraging Identity-Specific Facial Contours for Enhanced Heart Rate Estimation in Remote Photoplethysmography

Remote photoplethysmography (rPPG) estimates heart rate by capturing blood volume pulse (BVP) signals from subtle pixel variations in video frames. This study presents a novel approach that leverages facial physiological characteristics to improve heart rate estimation. Specifically, we introduce a method for extracting Identity-Specific Facial Contours (ISFCs) and utilize a self-learning combination mechanism that directs the model’s attention to these ISFCs, resulting in significant improvements in both accuracy and robustness. Our rFaceNet model effectively extracts ISFCs from temporally normalized frames using a Temporal Compressor Unit (TCU) and refines focus on relevant facial regions via a Cross-Task Feature Combiner (CTFC). Through meticulous training, rFaceNet significantly enhances the quality and interpretability of facial physiological signals when compared to previous approaches. Moreover, our method outperforms State-of-the-Art (SOTA) models across various heart rate estimation benchmarks.