To improve MTCNN’s face detection accuracy, this paper proposes multivariate branches block (MBB) and dilated convolutional block (DCB), and we design a cascaded multivariate branch convolutional neural network model(MB-MTCNN) based on these two structural blocks. First, the model consists of three subnetworks, and we replace the first convolutional block of each subnetwork with a dilated convolutional block. Second, replace the second convolutional block of the second and third subnetworks with MBB. In addition, we add a BN layer after each convolutional layer to accelerate the training speed of the model. Finally, we introduce a structural re-parameterization method to reduce the model’s overhead in inference. Experimental results show that our model increases the True Positive Rate (TPR) by 4.48% on the FDDB datasets, improves the average accuracy by 8.08%, 8.65%, and 10.29% on the three levels of validation sets of Wider Face datasets, 8.31% and 2.08% on the AFW and CelebA datasets, respectively. Moreover, based on structural re-parameterization, the model parameters during inference are 14.68% lower than training. This model is suitable for scenarios that require accurate detection of faces, such as crowded stations and traffic arteries.