The present application relates to facial recognition technology of a neural network. Disclosed is a multi-face detection method based on key point positioning. The method comprises: training a U-Net-based multi-face detection model by using a data set; inputting a picture to be subjected to detection into the trained U-Net-based multi-face detection model, so as to obtain a feature map in a heat map format; acquiring, by using a heat map maximum suppression algorithm, real facial key point position and facial average point position feature vectors from the obtained feature map in the heat map format; and according to the obtained real facial key point position and facial average point position feature vectors, determining a facial key point position by using an association algorithm, so as to complete facial detection. The present application further relates to blockchain technology. Data is stored in a blockchain. In the present application, a single model is used to simultaneously complete facial detection and facial key point positioning, thereby reducing calculation processes and steps, and achieving the technical effects of speeding up the response time of a final application and reducing the calculation consumption.
MULTI-FACE DETECTION METHOD AND SYSTEM BASED ON KEY POINT POSITIONING, AND STORAGE MEDIUM