Two-stage face sketch generation method capable of combining local and global characteristics

Abstract

The invention discloses a two-stage face sketch generation method capable of combining local and global characteristics. The two-stage face sketch generation method comprises the following steps: (1) dividing a sample; (2) dividing an image block; (3) dividing an image block subset; (4) generating an integral composite image block; (5) generating a final composite image block; and (6) compounding images. The two-stage face sketch generation method adopts a staging method, wherein the sample block is divided into a plurality of global subsets in a first stage, the initial image block is compounded in each subset, the sample block is divided into a plurality of local subsets in a second stage, the final image block is compounded in each subset, and the high-quality image with clean background and clear details can be compounded. The method only uses simple K mean value clustering and sample block position information to divide the sample block set, uses simple mapping to compound images, and greatly improves image compounding speed.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a two-stage human face portrait generation method combining local and global characteristics in the technical field of pattern recognition and computer vision. The invention can be used for face retrieval and recognition in the field of public security. Background technique [0002] In the criminal investigation and pursuit, the public security department has a database of citizens’ photos, combined with face recognition technology to determine the identity of criminal suspects. However, it is generally difficult to obtain photos of criminal suspects in practice, but they can be obtained with the cooperation of painters and witnesses. Sketch portraits of criminal suspects for subsequent face retrieval and recognition. Due to the great difference between portraits and ordinary face photos, it is difficult to obtain satisfactory recognition results by directly using...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the algorithm complexity is high and the image synthesis speed is slow

The disadvantage of this method is that because the method uses selective integration technology, the generated pseudo-portrait needs to be weighted and averaged, resulting in unclean background and unclear details, which in turn reduces the quality of the generated image.

The disadvantage of this method is that the quality of the synthesized image depends mostly on the initial image synthesis method, and the use of sparse representation for image synthesis will lead to too slow image synthesis

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