Face key point detection method and system based on local principal component analysis

Abstract

The invention discloses a face key point detection method and system based on local principal component analysis. The method comprises the steps of S1, collecting a large amount of human face image sample data, and marking the face key points; S2, dividing the face key points into a plurality of local key points, adopting the principal component analysis to process the local key points, and obtaining the principal component features of the local key points; S3, calculating a combination coefficient of the key points of each face image under the principal component characteristics; S4, constructing a regression model, training the model through the combination coefficient, and generating a combination coefficient regression model; S5, inputting a to-be-detected face image into the combination coefficient regression model, and predicting to obtain a combination coefficient; and S6, restoring the face key points based on the predicted combination coefficient and the principal component features. According to the present invention, the local principal component analysis is carried out on the key points, and the local principal component coefficients are predicted, so that the complexity of directly carrying out principal component analysis on all the key points is reduced, and the regression modeling precision is improved.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a face key point detection method and system based on local principal component analysis Background technique [0002] In recent years, there have been more and more researches on face analysis. The so-called face analysis refers to the recognition of human expressions, positions, identities, etc. based on the face, through computer vision and pattern recognition theory. Face key point detection is an important basic link in face recognition tasks. Accurate detection of face key points plays a key role in many practical applications and scientific research topics, such as face posture recognition and correction, expression recognition, mouth shape recognition, etc. . Therefore, how to obtain high-precision facial key points has always been a hot research issue in the fields of computer vision and image processing. Affected by factors such as face pose and occlusion, the ...

AI Technical Summary

Problems solved by technology

When performing array dimension, you can use a method based on category patterns according to different categories, which can overcome the problem that the traditional PCA algorithm cannot effectively use category information and poor robustness in the case of illumination and expression changes.

[0004] However, the above method directly performs PCA on all key points of the face, which will lead to the problem of low accuracy. This is because the changes of various parts of the face are various, so the dimension after arrangement and combination is higher.

Images