Face attribute invariant robustness face recognition method and system

Abstract

The invention discloses a face attribute invariant robustness face recognition method and system. The method comprises the following steps: S1, extracting face features; s2, estimating human face attributes; s3, constructing and selecting a face attribute sub-dictionary; s4, carrying out attribute-invariant face feature extraction; s5, carrying out face classification and recognition; in the system, a face depth feature extraction module is connected with a face attribute recognition module and a face attribute dictionary construction module; the face attribute recognition module is sequentially connected with the face attribute sub-dictionary selection module, the face attribute invariant feature extraction module and the face classification recognition module, and the face attribute sub-dictionary selection module is connected with the face attribute sub-dictionary selection module through the attribute over-complete dictionary. The method and system can solve the problem of face recognition in complex environments such as simultaneous changes of illumination, postures, expressions, shielding and resolution of the face and combination changes of the changes, keeps face attributesunchanged, and are high in robustness.

Description

technical field [0001] The present invention relates to the field of image processing and pattern recognition, in particular to a face recognition method and system with invariant face attributes and robustness. Background technique [0002] After more than half a century of development, face recognition technology has made substantial progress and has reached the application level in a controlled environment. However, in practical applications, especially in extremely complex environments, such as densely populated or highly mobile markets, shopping malls, supermarkets, etc., it is found that it is still not perfect, and it is easily affected by face lighting, posture, expression, occlusion, age and plastic surgery. and other comprehensive factors. Face recognition applications in complex environments are faced with various complex changes such as face illumination, posture, occlusion, and low resolution, making most face technology unusable at present. [0003] A large n...

AI Technical Summary

Problems solved by technology

That is, there is currently no method that can simultaneously solve the problem of face illumination, posture, expression, and resolution changes and their combination changes.

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