Single target portrait-based face portrait compositing method

Abstract

The invention discloses a single target portrait-based face portrait compositing method. The single target portrait-based face portrait compositing method comprises selecting a pair of photos from a database sample set as well as portraits inside portrait pairs as a target portrait; blocking the target portrait and dividing the blocked target portrait to obtain a first portrait block subset and a second portrait block subset; performing matrix decomposition on the obtained first portrait block subset to obtain a projection matrix, and projecting the second portrait block subset onto the projection matrix to obtain a first projection coefficient set; projecting portrait blocks, which are obtained by blocking a testing photo, onto the projection matrix to obtain a second projection coefficient set; searching for matched image blocks from the first projection coefficient set according to the similarity scale distance of the second projection coefficient set, composing the searched image blocks to obtain an initial composite image; performing iterative composition to obtain a final composite image. The single target portrait-based face portrait compositing method saves a process of taking time and labor to collect a large number of photos and portrait pairs and facilitates composition of portraits in various styles.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a face portrait synthesis method based on a single target portrait. Background technique [0002] Facial portrait synthesis has many important practical applications, such as digital entertainment. At present, social networks such as Twitter and Weibo are becoming more and more popular, and many users upload their own portraits to the Internet as account avatars to increase the entertainment effect. In order to reflect personalization and increase interest, users often prefer that their portraits have a variety of styles to choose from. In addition, the fewer constraints on the photos to be synthesized, the more users will prefer them. For example, the photos to be synthesized can be from various angles, expressions, backgrounds, full-body photos, group photos, etc. [0003] The current face portrait synthesis methods can be divided into the following three...

AI Technical Summary

Problems solved by technology

However, this method has the following disadvantages: each synthetic image block is synthesized separately, and the neighborhood relationship between the image blocks is not considered, resulting in an unnatural image at the fusion point and noise.

This method has the following defects: each test photo block finally selects only one training image block as the image block to be synthesized for image synthesis, resulting in block effects and missing details in the synthesized image

This method has the following defects: the quality of the synthesized image depends on the initial image synthesis method, and the linear superposition of detail information makes the synthesized image unnatural

[0007] The above three types of portrait synthesis methods require many photo-portrait pairs as training sets, but collecting photo-portrait pairs is time-consuming and laborious

In addition, when it is necessary to synthesize a certain type of portrait corresponding to the test photo, the above three types of portrait synthesis methods need to collect many corresponding types of photo-portrait pairs as a training set. However, in most cases, it is difficult to collect many specified Type of photo-portrait pair

In addition, the above three types of portrait synthesis methods are only applicable to the situation where the background of the test photos and the photos of the training set are the same. When the backgrounds of the test photos and the photos of the training set are different, there will be noise in the background of the synthetic portrait

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