Depth self learning-based facial beauty predicting method

Abstract

The invention discloses a depth self learning-based facial beauty predicting method. According to the depth self learning-based facial beauty predicting method of the invention, facial image local texture features extracted by an LBP operator have strong robustness to illumination and micro horizontal movement, so associative memorization of a network, for information representing facial beauty, can be facilitated when the facial image local texture features are adopted as the input of a CDBN, and therefore, possibility for the network to learn adverse feature description can be further decreased. A self-learning method can automatically improve the understanding of the CDBN for data feature distribution, and facial image beauty prediction can still obtain favorable facial image feature description under a condition that the type and number of training samples do not satisfy actual requirements. Through learning a deep nonlinear network structure, a network system, not relying on manual feature selection, combines low-level features such that more abstract and structural high-level distributed beauty feature representation can be formed, and therefore, one kind of automatic learning and feature extraction process can be expressed; and high consistency between facial image beauty mechanical scoring and manual scoring can be realized through using an SVM regression method.

Description

technical field [0001] The invention relates to a face beauty prediction method based on deep self-learning. Background technique [0002] It is human nature to love beauty, and everyone has the heart to love beauty. Aristotle said: "A beautiful face is a better testimonial". The favorable impression of beauty exists in daily life and has a great influence on people's daily life. The research on the beauty of human face is a frontier topic in the research on the nature and laws of human cognition that has emerged in recent years. Exploring how to better predict beauty will help the eternal theme of human beings, the code of human face beauty, to be scientific, objective and quantifiable. The description of human face beauty has made great progress in the interdisciplinary field of human face beauty research. [0003] At present, scholars at home and abroad mostly use geometric features or appearance features, and then use machine learning to predict the beauty of human fa...

AI Technical Summary

Problems solved by technology

However, the beauty of a human face largely depends on the local structure and contour information of the human face, while the mainstream methods based on appearance features only use primary features such as eigenfaces and texture features to represent facial beauty information, and do not involve more Structural, hierarchical feature expression

The traditional machine learning method for face beauty research requires a large number of training samples, and the degree of beauty requires a certain degree of discrimination, that is, the training samples should contain many labeled extremely beautiful and extremely ugly images, but in reality, most of the face images are neutral beauty. , it is difficult to find many extremely beautiful and extremely ugly images, which will undoubtedly bring difficulties to the research work

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