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Face identification method based on iteration re-constraint group sparse expression classification

A technology of face recognition and sparse representation coefficient, applied in the field of face recognition and target recognition, which can solve the problems of high computational complexity and low classification and recognition rate.

Active Publication Date: 2017-01-04
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0006] The present invention can solve the problems of low classification and recognition rate and high computational complexity of some large-area occluded images, high-complexity congested images, camouflage images or images with dramatic expression changes in the existing face recognition technology, and provides A Face Recognition Method Based on Iteratively Reconstrained Group Sparse Representation Classifier with Adaptive Feature Weight Learning

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  • Face identification method based on iteration re-constraint group sparse expression classification
  • Face identification method based on iteration re-constraint group sparse expression classification
  • Face identification method based on iteration re-constraint group sparse expression classification

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Embodiment Construction

[0084] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0085] A face recognition method for iteratively re-constrained group sparse representation classification, including the dictionary set training process, the initial value calculation process of coefficients and weights, the update process of coefficients and weights, and the image classification process:

[0086] a) dictionary set training process: randomly select image samples, divide them into c classes according to their category information, and form a training dictionary set X=[X 1 ,X 2 ,...,X c ]∈R m×n , each class has its own sample label. where X i =[x i1 ,x i2 ,...,x ini ]∈R m×ni is a sample subset, i∈1,2,...,c. x ij ∈ R m is the j-th sample in the i-th class with dimensions m, n i Represents the ordinal number of training samples in the i-th category, n=∑ i＝1 c no i is the total number of samples;

[0087] b) The in...

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Abstract

The invention provides a face identification method based on iteration re-constraint group sparse expression classification. Therefore, large-area shielding images, high-complexity congestion images, camouflage images or images with drastic expression change can be effectively classified. With an objective of obtaining the higher identification rate, the method comprises following steps of: a) randomly selecting image samples for classification, and grouping the image samples into a training dictionary set, wherein each type is provided with the corresponding sample label; b) calculating an initial value of a residual value e and a sparse expression coefficient theta generated by comparing a to-be-tested sample with each type in the dictionary set, and calculating a weight initial value of the residual value e and the sparse expression coefficient theta; c) carrying out iteration calculation on the residual value e of each type, the sparse expression coefficient theta and their weight values, repeating the iteration process until reaching a convergence condition or the biggest iteration number, and outputting the final theta value; and d) classifying the to-be-tested sample according to the smallest e value so as to obtain an identification result to classify the to-be-tested sample.

Description

technical field [0001] The present invention is a face recognition method, specifically, a face recognition method of iterative heavy constraint group sparse representation classification of adaptive weight learning, which relates to the field of pattern recognition and can be used for face recognition and target recognition Wait. Background technique [0002] In today's society, identity verification has a very important value. In recent years, human biological characteristics have been more and more widely used in personal identification. Compared with traditional methods, using human biological characteristics for identification is more secure, reliable, unique, and highly stable, and is less likely to be stolen and crack. For face recognition, it involves a wide range of fields, including biology, physiology, psychology, cognition, graphic imagery, pattern recognition and other fields, and it is closely related to the identification and identification methods of biolog...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06K9/00
CPCG06V40/172G06V10/40G06V10/513G06F18/28G06F18/24
Inventor 郑建炜杨平邱虹陈婉君
Owner ZHEJIANG UNIV OF TECH
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