Recent advancements in remote photoplethysmography (rPPG) have enabled the extraction of blood volume pulses (BVP) from facial videos, facilitating the measurement of vital physiological indicators such as heart rate, blood oxygen, and blood pressure. However, achieving accurate blood pressure measurement requires more than just proximity to the true frequency, it also necessitates improving the precision of rPPG signal waveform features predicted from facial videos. This paper introduces an enhanced hybrid convolutional neural network (CNN) model featuring a dual-branch architecture, augmented by a skin color difference amplification module. By leveraging a designed waveform consistency loss function during training, the proposed model substantially enhances the accuracy of rPPG signal predictions. Our research aims to furnish a high-precision rPPG signal as a dependable prerequisite for subsequent physiological indicator measurements, notably in blood pressure. Evaluation on the UBFC-RPPG and V4V datasets demonstrates the model’s adeptness at capturing rPPG signals closely resembling labeled signals. Notably, the resulting signals exhibit mean absolute errors of 1.14 and 2.74, respectively, in heart rate measurement, underscoring the model’s robust generalization capability.