Training and detecting methods and systems for key human facial feature point detection model

Abstract

The present invention provides a training method and system and a detecting method and system for a key human facial feature point detection model. The training method comprises: acquiring a human face position of an input picture; obtaining an initial position of a key feature point before updating according to an average key feature point of a training set and the face position; obtaining an initial position of a key feature point after updating according to a position of an authentic key feature point; according to a difference value between the initial positions of the key feature points before and after updating and a region feature extracted before updating, training a dynamic initialization regression model; and training a cascade regression model according to a distance difference between the initial position of the key feature point after updating and the position of the authentic key feature point and a region feature extracted after updating. The detecting method comprises: calling the dynamic initialization regression model and the cascade regression model in turn for a to-be-detected picture, and calculating a position of a key human facial feature point; and determining whether the key facial feature point is accurate according to a comparison with a preset point. By using the methods and systems provided by the present invention, the accuracy of detecting the key human facial feature point is improved.

Description

technical field [0001] The invention relates to the field of computer vision processing, in particular to a human face key feature point detection model training method and system, detection method and system. Background technique [0002] The key feature points of the face are the basis of face processing technologies such as face recognition and expression recognition. The performance of facial feature point positioning greatly affects the accuracy of face detection methods. Among all facial feature points, salient key feature points such as eyes, mouth, nose tip and eyebrows are the most important, and the distance ratio between them is used to distinguish faces. For general applications, salient key feature points can already meet the needs of processing methods, can align and normalize faces of different shapes and sizes, and provide information for further processing. In addition, the six points of left / right eyes, mouth, nose tip and eyebrows can also be used as the ...

AI Technical Summary

Problems solved by technology

[0006] The existing methods for detecting key feature points of faces can be summarized as follows: firstly, according to the feature information of the initial key feature points of the training set and the real key feature points, the corresponding regression model is trained; secondly, through feature extraction and combined with the regression model, find The best position of each key feature point; although the detection accuracy of the key feature points of the face is improved under multi-angle and illumination, there are still the following shortcomings: First, for the key features of the face with large angles and exaggerated expressions Point detection, the detection accuracy is far from enough; second, compared with the salient key feature points, the detection accuracy of the non-significant key feature points is poor; third, for people with uneven lighting distribution or dark light face, the detection performance is poor; fourth, the discrimination accuracy of the confidence of key feature points is not high, and false detection is easy to occur

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