A method for processing human face images and related equipment

Abstract

The present application discloses a face image processing method and related equipment, including: firstly acquire a first image set and a second image set, the first image set includes a plurality of real face images, and the second image set includes a plurality of pixel Face image; then randomly generate semantic information vectors; then input multiple real face images in the first image collection, multiple pixel face images in the second image collection, and semantic information vectors into the model to be trained for training, and obtain Image pixelation model; Finally, according to the image pixelation model, the first human face image to be recognized is processed to obtain the display image of the first human face image, and the display image is used to realize the target human face in the first human face image enhanced. By adopting the embodiment of the present application, it is possible to improve the pixelation effect of the face image and improve the recognition degree of the face image.

Description

technical field [0001] This application relates to the fields of artificial intelligence and bionic vision, and in particular to a processing of human face images and related equipment. Background technique [0002] At present, the pixels of artificial retinal imaging images are generally below 1000+, and due to the existing electrode manufacturing, energy transmission, implant packaging technology level and safety, etc., it is still difficult to make a leap in the imaging pixels of bionic visual prosthesis sexual enhancement. The low pixels of bionic visual prosthesis images will lead to a large loss of image information, making it difficult for users to correctly identify facial expressions and identities. To solve this problem, the existing technology adopts the method of pre-correcting the image information of the acquired face image and then simplifying and enhancing the image information. Specific face information. In addition, the existing technology also provides ...

AI Technical Summary

Problems solved by technology

[0002] At present, the pixels of artificial retinal imaging images are generally below 1000+, and due to the existing electrode manufacturing, energy transmission, implant packaging technology level and safety, etc., it is still difficult to make a leap in the imaging pixels of bionic visual prosthesis sexual enhancement

The low pixels of bionic visual prosthesis images will lead to a large loss of image information, making it difficult for users to correctly identify facial expressions and character identities

To solve this problem, the existing technology adopts the method of pre-correcting the image information of the acquired face image and then simplifying and enhancing the image information. Specific face information

In addition, the existing technology also provides a scheme to exaggerate the human face to improve the face recognition rate under the low-resolution artificial retina, but there is currently a lack of general and effective face caricature tools and methods. method

The existing technology also proposes to use the image meaning recognition module to recognize the meaning in the image, and design a few individual pixels as indicator lights to represent different human faces, but the implementation method of the meaning recognition module has not been given yet. Moreover, the facial feature information that can be represented by the indicator light is extremely limited, which cannot meet the requirements for face recognition of different expressions, different ages and different genders.

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