Aiming at the problem that the high-performance face detection model has a large number of parameters,high computational complexity,and it is difficult to deploy on the edge of embedded devices,a lightweight face detection algorithm is proposed,which is based on the lightweight improvement on RetinaFace.Firstly,MobileNetV2_0.5× is used as the backbone to extract features,and PANLite with few parameters is selected for two-way fusion of multi-scale features to enhance the ability of feature representation.RFBLite is used as the feature enhancement module to incorporate context information while increasing the feature receptive field.Finally,filter pruning algorithm is applied in the trained model.Then the pruned model is retrained for fine-tuning and depolyed to Nvidia Jetson Nano for model inference.The experiment results show that the lightweight model can achieve high face detection performance with a small amount of parameters and low computational complexity,and can perform real-time inference on embedded platforms.