Temporal Hierarchical Quick Spatial Attention Mechanism for Heart Rate Measurement

Remote photoplethysmography (rPPG) is a promising non-contact method for measuring heart rate (HR) and physiological signals. However, current deep learning approaches in this field primarily focus on extracting subtle rPPG cues using convolutional neural networks with limited spatio-temporal receptive fields. This approach overlooks the importance of long-distance temporal perception and interaction, which are crucial for accurate rPPG modeling. In this paper, we present an end-to-end temporal hierarchical quick spatial attention (THQSA) network to adaptive aggregate the local and global temporal features through multi-scale temporal slice perception and interaction. Specifically, the Temporal Hierarchical Block (THB) is firstly constructed to incorporate the global periodic rPPG attention features across multi-scale temporal slices attention mechanism. And then THB refines local temporal slice representations to account for disturbances. Quick spatial attention is further developed to ensure the model obtains better global and local feature representation by avoiding irrelevant features. Additionally, we apply the rPPG signal to a 1-D convolutional network for emotion recognition. Promising results are achieved in the emotion test. To demonstrate the effectiveness of our approach, extensive experiments conducted on two benchmark datasets showcase an outperformance over existing state-of-the-art methods.