Face image synthesis method based on adaptive expression

Abstract

The invention discloses a face image synthesis method based on adaptive expression. By using the method, problems that synthesized image definition is low and details are not complete in an existing method are solved. The method comprises the following steps of firstly, processing a database, carrying out image filtering on all the images, then partitioning the images and extracting an image block characteristic so as to obtain one training image block dictionary and two photograph block dictionaries; secondly, testing whether a photograph block includes edge information or face characteristic information, according to that, selecting different dictionaries and searching a neighbor block; and finally, using a Markov network model to obtain image blocks to be synthesized, fusing all the image blocks to be synthesized and acquiring the synthesized image. Compared to the prior art, by using the method, a synthesis result possesses high definition and complete details and can be used for face retrieval and identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a face image synthesis method in the technical field of pattern recognition and computer vision, which can be used for face retrieval and recognition in criminal investigation and case solving. Background technique [0002] With the development of modern society and multimedia, more and more video images of people are recorded, how to identify and authenticate a person's identity based on existing images has become one of the problems to be solved, among which face recognition has Features such as directness, friendliness and convenience have been extensively researched and applied. One of the important applications of face recognition technology is to assist the police in criminal investigation and detection. However, in many cases, it is difficult to obtain photos of suspects in some serious cases, or the obtained photos are not frontal or the lighting is uneven...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that because the number of neighbors is fixed, the synthesis result has defects of low definition and blurred details.

However, the disadvantage of this method is that only one training image block is selected for each photo block position for image synthesis, resulting in block effects and missing details in the synthesis results.

However, the disadvantage of this method is that it ignores the relationship between adjacent image blocks, resulting in blurring and block effects in the synthesis results.

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