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.