AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching
This work addresses remote healthcare applications by improving heart rate measurement in less-constrained scenarios, but it appears incremental as it builds on existing end-to-end methods with enhancements.
The paper tackled the problem of remote heart rate measurement from facial videos being vulnerable to challenging conditions like head movement and bad illumination, and established a strong end-to-end baseline (AutoHR) using neural architecture search, achieving superior performance on intra- and cross-dataset testing.
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications (e.g., remote healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and bad illumination). In this letter, we explore the reason why existing end-to-end networks perform poorly in challenging conditions and establish a strong end-to-end baseline (AutoHR) for remote HR measurement with neural architecture search (NAS). The proposed method includes three parts: 1) a powerful searched backbone with novel Temporal Difference Convolution (TDC), intending to capture intrinsic rPPG-aware clues between frames; 2) a hybrid loss function considering constraints from both time and frequency domains; and 3) spatio-temporal data augmentation strategies for better representation learning. Comprehensive experiments are performed on three benchmark datasets to show our superior performance on both intra- and cross-dataset testing.