A facial feature point positioning method and apparatus, a computing device and a storage medium. Said method comprises: inputting a target facial image of a feature point to be positioned into a first convolutional neural network model, so as to obtain a first shallow feature image which contains a plurality of candidate feature points and is outputted by a predetermined convolutional layer of the first convolutional neural network model (210); using a bilinear interpolation algorithm to perform bilinear interpolation on the candidate feature points in the first shallow feature image, so as to obtain a second shallow feature image (220); and inputting the second shallow feature image into a second convolutional neural network model cascaded with the first convolutional neural network model, so as to obtain a facial feature image which corresponds to the target facial image of the feature point to be positioned and is outputted by the second convolutional neural network model (230), the weight of a predetermined convolutional layer of the second convolutional model and the weight of each convolutional layer before same being consistent with the weight of the convolutional layer in a corresponding layer number of the first convolutional model, respectively. By means said method, the positioning precision of the facial feature point is improved, and since convolution layers of cascaded models share weights, the calculation amount and the parameter amount can be reduced, and the training speed and convergence speed of the models can be improved.
FACIAL FEATURE POINT POSITIONING METHOD AND APPARATUS, COMPUTING DEVICE, AND STORAGE MEDIUM