Method and device for positioning face key points in image

Abstract

The invention discloses a method and device for positioning face key points in an image. The method includes the following steps that: face detection is performed on a target image to be detected, so that a face region range can be determined; a first prediction model is utilized to determine the initial positions of face key points in the face region range; and a second prediction model is utilized to carry out a plurality of times of iterative operation based on the pixel values of pixels in the face region range and the initial positions of the face key points, and the positions of the face key points are updated step by step, and when the iterative operation terminates, the final positions of the face key points in a face region frame are determined, when the iterative operation is carried out for the first time, the second prediction model adopts shape index features extracted at the initial positions as input. With the method and device provided by the implementations of the invention adopted, the face key points can be positioned more accurately.

Description

technical field [0001] The present application relates to the technical field of human face key point positioning, and in particular to a method and device for human face key point positioning in an image. Background technique [0002] With the development of science and technology, images play an important role in information dissemination. Among them, many images are related to human faces, and in practical applications, there are many occasions where automatic facial key points (mainly eyes, nose, mouth and human face contour points) positioning is required. For example, these occasions include: facial expression synthesis, facial attribute analysis (including analysis of gender, expression type, etc.), 3D facial restoration, and so on. However, the characters in the image often have different poses, exaggerated expressions and complex backgrounds, making it difficult to accurately locate the key points of the face. [0003] Traditional facial key point localization alg...

AI Technical Summary

Problems solved by technology

However, this algorithm initializes the average face shape according to the face detection frame as the initial point of the algorithm iteration, resulting in the accuracy of the algorithm heavily dependent on the position and size of the face detection frame. When the actual face detection frame and the training face image are calibrated When the face detection frame is inconsistent, face key point positioning often fails

However, in the existing technology, a roughly rectangular area is generally identified through basic face recognition technology, and this area is used as a face detection frame as a starting point for algorithm iteration. Obviously, this rectangular area is often not accurate enough. , therefore, the final face key point positioning results are often not accurate enough,

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