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Robust human face recognition method based on weighted mixing norm regression

A face recognition and robust human technology, applied in the field of robust face recognition, can solve the problems of continuous and non-continuous noise interference of images, and achieve the effects of high accuracy, solving the problem of face image loss, and high computing efficiency

Active Publication Date: 2018-09-11
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] The present invention solves the problem of continuous and discontinuous noise interference in images in real face recognition technology, and provides a robust face recognition method based on weighted mixed norm regression, which can be used for face detection and face classification

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  • Robust human face recognition method based on weighted mixing norm regression
  • Robust human face recognition method based on weighted mixing norm regression
  • Robust human face recognition method based on weighted mixing norm regression

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Embodiment Construction

[0015] The present invention will be further described below in conjunction with the accompanying drawings. A robust face recognition method based on weighted mixed norm regression, comprising the following steps:

[0016] a) Pick n for each target object i samples as the training set, determine the dictionary matrix X, and input the test face image Y;

[0017] b) Establish the WMNR model, optimize the model, and obtain the corresponding feature weight matrix W, coefficient vector a and non-negative weight vector s;

[0018] c) Substituting the W, a and s learned in step b into the classification reconstruction error minimization model, and finally realizing face image recognition.

[0019] The process of said step a specifically includes the following:

[0020] Pick n for each target object i samples as the training set, each sample dimension is m, where i=1,...,c, c is the number of target categories, the total training sample size is Thus, determine the dictionary mat...

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Abstract

A robust human face recognition method based on weighted mixing norm regression relates to the field of pattern recognition, aiming to improving the recognition precision of the face image loss. The method comprises the following steps: a) selecting ni samples as a training set for each target object, determining a dictionary matrix X, and inputting a test face image Y; b) establishing a WMNR model, optimizing the model, and obtaining a corresponding characteristic weight matrix W, a coefficient vector a and a non-negative weight vector s; c) and substituting the W, a and s obtained in the step b into a classification reconstruction error minimization model, and finally realizing the face image recognition. According to the invention, the model is optimized by using a value self-adaptationmechanism and an effective weighted fast iterative algorithm, has the advantages of being high in operation efficiency, high in accuracy, and is very suitable for face detection and face classification under the condition of image damage.

Description

technical field [0001] The invention relates to a computer vision robust face recognition method, which can be used for face recognition, image recognition, target recognition and the like. Background technique [0002] With the rapid development of the Internet economy, identity verification is of great value. In recent years, human biometrics have been more and more widely used in personal identification. Compared with traditional methods, they are safe, reliable, unique and highly stable, and are not easy to be stolen or cracked. The inherent biological characteristics of human beings mainly include: DNA, fingerprint, iris, voice, gait, palm print, face, etc., based on people's cognition of independent individual characteristics, combined with advanced computer technology and pattern recognition theory, such as DNA recognition Technology, fingerprint recognition technology, face recognition technology and so on have been developed one after another. As far as the curren...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/172G06F18/214
Inventor 郑建炜路程秦梦洁张晶晶杨弘陈婉君李宏凯
Owner ZHEJIANG UNIV OF TECH
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