Image recognition method, device, system and computer storage medium

Abstract

The invention provides an image recognition method, device, system and computer storage medium. The method includes: acquiring an image to be recognized and a target image, performing a full-image recognition step, comparing the image to be recognized with the target image, and performing feature position comparison when the comparison result of the full image does not meet the preset conditions, and performing a feature position comparison to identify the target image. The characteristic position of the image is compared with the characteristic position of the target image, and when the comparison result of the characteristic position satisfies the preset condition, the comparison result obtained by the comparison of the characteristic position is output. The method greatly improves the extraction efficiency of effective information.

Description

technical field [0001] The present invention relates to the field of image recognition, and more particularly relates to an image recognition method, device, system and computer storage medium. Background technique [0002] At present, image recognition systems, especially face recognition systems, are increasingly used in scenarios that require identity verification in the security and financial fields, such as remote bank account opening, access control systems, and remote transaction operation verification. In these high-security application fields, in addition to ensuring that the face similarity of the person being verified conforms to the base library stored in the database, it is first necessary to verify that the person being verified is a legal living creature. In other words, the face recognition system needs to be able to prevent attackers from using photos, videos, 3D face models, or masks to attack. [0003] In image recognition, especially face recognition tec...

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Problems solved by technology

[0004] However, the face recognition in the above technologies is based on extracting features from a single face image, and then comparing based on the face features. The two images used for comparison do not interact when extracting features.

In this way, the importance of facial features cannot be determined according to the actual situation when extracting features. For example, one of the pictures is a person wearing a mask, and the other picture is a person not wearing a mask. The mouth features are extracted from the two pictures separately, then the comparison of the features will be meaningless, so the extraction efficiency of effective features will be reduced

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