Research on Heart Rate Detection from Facial Videos Based on an Attention Mechanism 3D Convolutional Neural Network

Remote photoplethysmography (rPPG) has attracted growing attention due to its non-contact nature. However, existing non-contact heart rate detection methods are often affected by noise from motion artifacts and changes in lighting, which can lead to a decrease in detection accuracy. To solve this problem, this paper initially employs manual extraction to precisely define the facial Region of Interest (ROI), expanding the facial area while avoiding rigid regions such as the eyes and mouth to minimize the impact of motion artifacts. Additionally, during the training phase, illumination normalization is employed on video frames with uneven lighting to mitigate noise caused by lighting fluctuations. Finally, this paper introduces a 3D convolutional neural network (CNN) method incorporating an attention mechanism for heart rate detection from facial videos. We optimize the traditional 3D-CNN to capture global features in spatiotemporal data more effectively. The SimAM attention mechanism is introduced to enable the model to precisely focus on and enhance facial ROI feature representations. Following the extraction of rPPG signals, a heart rate estimation network using a bidirectional long short-term memory (BiLSTM) model is employed to derive the heart rate from the signals. The method introduced here is experimentally validated on two publicly available datasets, UBFC-rPPG and PURE. The mean absolute errors were 0.24 bpm and 0.65 bpm, the root mean square errors were 0.63 bpm and 1.30 bpm, and the Pearson correlation coefficients reached 0.99, confirming the method's reliability. Comparisons of predicted signals with ground truth signals further validated its accuracy.