Visual Heart Rate Estimation with Convolutional Neural Network

By Radim Spetlik, Vojtech Franc, Jan Cech, Jiri Matas. British Machine Vision Conference, 2018

323 citations recorded by the source corpus.

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Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks (2019, methodology): “In order to make fair comparison with previous works [3, 12, 18], we follow the same routine as their works and use 30 seconds clip (frames 306 to 2135) of each video.”
Remote Heart Rate Measurement From Highly Compressed Facial Videos: An End-to-End Deep Learning Solution With Video Enhancement (2019, methodology): “Compared with previous works which only output simple HR numbers[17, 25], the proposed rPPGNet produces much richer rPPG signals with curve shapes and peak locations.”
Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (2020, methodology): “Neural network based approaches have been successfully applied using the BVP or respiration as the target signal [9, 18, 19, 20], but these methods still struggle with effectively combining spatial and temporal information while maintaining a low computational budget.”
PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, unknown): “In the rPPG measurement task, the supervision signals from temporal domain (e.g., mean square error loss [10], negative Pearson loss [64, 66]) and frequency domain (e.g., cross-entropy loss [45,62], signal-to-noise ratio loss [52]) provide different extents of constraints for model learning.”