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The majority of non-contact heart rate detection are based on pure traditional or pure deep learning methods, which struggle with high noise interference and low accuracy in complex scenes or with insufficient training data. Therefore, this paper proposes a hybrid method that combines traditional signal processing with deep learning, leveraging the complementary advantages of both, to enhance heart rate detection accuracy and robustness. First, Gaussian heatmaps are used to predict the facial keypoints for Region of Interest (ROI) localization, from which the skin color change signals of RGB channels are extracted. After using Fast-ICA to demix the pixel averages of the three channels, three independent source signals are obtained, and the signal with the highest signal-to-noise ratio is selected as the remote photoplethysmography (rPPG) signal. Then Gramian angular field method (GAF) is used to encode the feature image of rPPG signal as the input of LA-Res2Net, and finally the model can infer the heart rate after training. The effectiveness of the proposed method is verified using the VIPL-HR-V2 dataset. Experimental results show that compared with traditional image processing methods and other advanced models, the proposed method reduces the mean absolute error (MAE) from 15.2 BPM to 6.2 BPM, the root mean square error (RMSE) from 19.5 BPM to 9.1 BPM, and increases the Pearson correlation coefficient (R) from 0.22 to 0.62. This indicates that the proposed method provides a reliable solution for remote non-contact human heart rate detection.

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