Remote photoplethysmography (rPPG) technology has become increasingly popular
due to its non-invasive monitoring of various physiological indicators, making
it widely applicable in multimedia interaction, healthcare, and emotion
analysis. Existing rPPG methods utilize multiple datasets for training to
enhance the generalizability of models. However, they often overlook the
underlying conflict issues across different datasets, such as (1) label
conflict resulting from different phase delays between physiological signal
labels and face videos at the instance level, and (2) attribute conflict
stemming from distribution shifts caused by head movements, illumination
changes, skin types, etc. To address this, we introduce the DOmain-HArmonious
framework (DOHA). Specifically, we first propose a harmonious phase strategy to
eliminate uncertain phase delays and preserve the temporal variation of
physiological signals. Next, we design a harmonious hyperplane optimization
that reduces irrelevant attribute shifts and encourages the model's
optimization towards a global solution that fits more valid scenarios. Our
experiments demonstrate that DOHA significantly improves the performance of
existing methods under multiple protocols. Our code is available at
this https URL

DOHA 框架通过引入和融合相干相位策略和相干超平面优化，解决了不同数据集之间的标签冲突和属性冲突，提升了现有方法在多个协议下的性能表现。