基于TDM-EfficientPhys的人脸视频HR检测方法

Face video HR detection method based on TDM-EfficientPhys

摘要:

为了实现高精度的人脸视频心率(heart rate,HR)检测,本文提出了一种基于改进EfficientPhys网络的人脸视频HR检测方法:TDM-EfficientPhys。首先,引入时间导数网络(temporal derivative network, TDM-Net),有效地保留了远程光电容积脉博波描记术 (remote photoplethysmography,rPPG)信号的时间特征。然后,调整EfficientPhys的Dropout参数,嵌入残差结构,有效地避免梯度消失和网络退化问题,并且将TDM-Net和改进后的EfficientPhys网络以跳跃连接的方式实现时序和空间信息的特征融合,提高检测精度。最后,使用两个公开的数据集PURE和UBFC-rPPG来验证本文所提出模型的有效性,实验表明,本文方法测试的均方根误差(RMSE)降低至2.387,平均绝对误差(MAE)降低至2.040,与现有的模型相比,本文方法显示了更好的网络性能和更精准的HR。

Abstract:

To achieve high-precision detection of heart rate (HR) from face videos,this paper proposes a face video HR detection method based on the improved EfficientPhys network:TDM-EfficientPhys.First,the temporal derivative network (TDM-Net) is introduced to effectively preserve the temporal characteristics of the remote photoplethysmography (rPPG) signal.Then,the Dropout parameter of EfficientPhys is adjusted to embed the residual structure,which effectively avoids gradient disappearance and network degradation problems.In addition,TDM-Net and the improved EfficientPhys network are connected through skip connections to realize feature fusion of time series and spatial information to improve detection accuracy.In the end,two publicly available datasets,PURE and UBFC-rPPG,are used to validate the proposed model in this paper. Experiments show that root mean square error (RMSE) of the method tested in this paper is reduced to 2.387 and mean absolute error (MAE) is reduced to 2.040,which shows better network performance and more accurate HR compared with existing models.