Face beauty prediction method based on multi-task transfer learning

A technology of multi-task learning and transfer learning, applied in the field of face beauty prediction based on multi-task transfer learning, can solve the problems of small database size, prone to over-fitting, difficulty in deep network models, etc., and achieve good model generalization Ability to utilize and improve the effect of accuracy

Inactive Publication Date: 2019-08-13
WUYI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the use of deep learning methods for face beauty research requires a large number of training samples, and the existing databases for face beauty prediction research are generally small, so it is not only difficult to directly train a deep network mode

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  • Face beauty prediction method based on multi-task transfer learning
  • Face beauty prediction method based on multi-task transfer learning
  • Face beauty prediction method based on multi-task transfer learning

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Embodiment Construction

[0041] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0042] Such as figure 1 As shown, the present invention provides a face beauty prediction method based on multi-task transfer learning. The present invention enhances the accuracy of face beauty prediction by adding expression recognition and age recognition; Fitting, and the computing equipment is not enough, this project intends to use mainstream VGG, ResNet, GoogleNet and other backbone deep convolutional networks as shared feature learning network structure, and through model migration, the trained convolutional network will be trained to transfer shared features. During the training process, network parameters are shared between each task, and shared features are learned, thereby improving the accuracy of the network for single-task learning. Specifically include the following steps:

[0043] S1), constructing multi-task learning face datab...

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Abstract

The invention provides a face beauty prediction method based on multi-task transfer learning, and the method comprises the steps: building a multi-task face database, carrying out the feature learning, carrying out the feature fusion, and building a face beauty prediction model. The method improves the accuracy of face beauty prediction through improving the expression recognition and age recognition. In order to avoid over-fitting of a deep network trained by a small amount of sample data and insufficient computing equipment, a VGG, ResNet and GoogleNet backbone deep convolutional network isused as a shared feature learning network structure, model migration is used, and the trained convolutional network is used to train a migratable shared feature. Network parameters are shared among tasks in the training process, and shared characteristics are learned, so that the accuracy of single task learning of the network is improved. Through using the deep learning network for multi-task learning, the shared representation layer can enable the tasks with universality to be better combined with the correlation information, and the task specific layer can independently model the task specific information.

Description

technical field [0001] The invention relates to the technical field of face beauty evaluation using image processing and machine learning technology, in particular to a face beauty prediction method based on multi-task transfer 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. 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 measure beauty will help to obtain scientific, objective and quantifiable human face beauty codes, an eternal theme of human beings. The description of human face beauty has made great progress in the interdisciplinary field of human face beauty research. [0003] In real life, people have different ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/178G06V40/174G06V40/161G06V40/168G06N3/045G06F18/214
Inventor 甘俊英项俐麦超云
Owner WUYI UNIV
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