Face key point positioning model training method and device, apparatus and storage medium

Abstract

The embodiment of the present application discloses a face key point positioning model training method and device, an apparatus and a storage medium. The training method includes: constructing a CNN model for face key point positioning, wherein the number of convolution layers of the CNN model is greater than a first threshold, and the number of channels of the convolution layers is less than a second threshold; using the CNN model to perform face key point positioning on training samples, and obtaining prediction positions of face key points, wherein the face key points include n types, and the n is an integer greater than 1; calculating respective loss function values corresponding to the n types according to prediction positions and real positions of each type of face key points, and calculating a loss function value of the CNN model; and stopping training the CNN model and saving the CNN model if the loss function value of the CNN model is less than a preset threshold. The embodiment of the present application reduces the model size by constructing the elongated CNN model while ensuring that the positioning accuracy is not lost as much as possible.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image recognition, and in particular to a training method, device, equipment and storage medium for a human face key point location model. Background technique [0002] Face key point positioning, also known as face facial features positioning, refers to identifying the positions of key points such as eyes, eyebrows, nose, and mouth from a face image. Face key point positioning technology is the basic technology of software such as beauty makeup, face decoration, face special effects, and face AR (Augmented Reality, Augmented Reality). [0003] The traditional face key point location technology is realized based on some image processing algorithms, such as SDM (Supervised Descent Method, Supervised Descent Method). By extracting the features in the face image, such as LBP (Local Binary Pattern, Local Binary Pattern), HOG (Histogram of Oriented Gradient, Histogram of Oriented G...

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Problems solved by technology

[0005] However, the parameters of the above model are relatively large, which makes the model not suitable for deployment in mobile terminals such as mobile phones, which puts higher requirements on the storage capacity of mobile terminals

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