Face data identity recognition method based on generative adversarial network

Abstract

The invention discloses a face data identity recognition method based on a generative adversarial network, and relates to the technical field of biological feature recognition and artificial intelligence security, and the method comprises the steps: firstly constructing an image coding-generative network, respectively extracting an attribute feature code and an expression pose code of a face image through two coding networks; combining to obtain a first implicit vector, obtaining a second implicit vector through the mapping network, sending the second implicit vector into the generation network to obtain an output image, completing fusion of facial image attribute features and expression poses, and realizing identity recognition of human eye vision by using a face changing technology; and secondly, constructing an adversarial vector mapping network, inputting a second implicit vector into the adversarial vector mapping network to obtain an adversarial implicit vector, obtaining an adversarial sample image with a relatively large identification result difference of the face identification model and a relatively small human vision difference through a generative network, and realizing identity recognition of the face identification model through an adversarial sample technology.

Description

technical field [0001] The invention relates to the technical fields of biological feature recognition and artificial intelligence security, in particular to a face data identity de-recognition method based on a generative confrontation network. Background technique [0002] In the era of artificial intelligence based on big data training, computer vision technology is widely used in security and tracking task scenarios, but the resulting security problems of misuse of face data have aroused people's concern about the necessity of protecting face privacy. Pay attention to. Traditional anonymized face technologies, such as mosaicing or blurring faces, have the disadvantage of large information loss, making it impossible for users or data developers to effectively use the anonymized face data. With the introduction of the concepts of adversarial samples and generative adversarial networks, two face recognition (De-identify, De-id) technologies represented by adding random noi...

AI Technical Summary

Problems solved by technology

However, the disturbance of random noise will cause the image quality to degrade, and the face-changing technology will be at the cost of completely losing the original face information.

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