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code of BMVC2019 paper 'Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks'

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PhysNet

Main code of BMVC2019 paper 'Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks'

How to train it?

#1. Inference the model
model = PhysNet_padding_Encoder_Decoder_MAX(frames=128)
rPPG, x_visual, x_visual3232, x_visual1616 = model(inputs)

#2. Normalized the Predicted rPPG signal and GroundTruth BVP signal
rPPG = (rPPG-torch.mean(rPPG)) /torch.std(rPPG)	 	# normalize
BVP_label = (BVP_label-torch.mean(BVP_label)) /torch.std(BVP_label)	 	# normalize

#3. Calculate the loss
loss_ecg = Neg_Pearson(rPPG, BVP_label)

It is just for research purpose, and commercial use is not allowed.

If you use the PhysNet please cite:

@inproceedings{yu2019remote,
    title={Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks},
    author={Yu, Zitong and Li, Xiaobai and Zhao, Guoying},
    booktitle= {Proc. BMVC},
    year = {2019}
}

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code of BMVC2019 paper 'Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks'

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