Constraint-based face identification method for single test sample

Abstract

The invention provides a constraint-based face identification method for a single test sample. The method comprises the following steps: step 1, performing preprocessing on a training sample set and a test sample; step 2, in a first phase of an algorithm, designing a function relation between the test sample and a training sample, and establishing the function relation between the test sample and the training sample through a method of representing the test sample by use of the training sample in a linearity mode; step 3, designing a class embedded item of a dictionary; step 4, in a second phase of the algorithm, learning the dictionary which is also optimal expression of the test sample by use of the training sample set; and step 5, a classification method, i.e., classifying the test sample by use of a linearity classification method. A test result indicates that the algorithm which is brought forward has the advantages of low calculation complexity, high reconstruction performance, high identification performance and high compactness and the like, and can further improve the face identification rate.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a constraint-based dictionary learning method for a single test sample. Background technique [0002] The constraint-based dictionary learning theory has opened up a new research direction for exploring the features in the data and enhancing the discrimination of the dictionary. Learn to deal with a series of unresolved problems separately. For this reason, the present invention proposes a single test sample-oriented dictionary learning algorithm based on atomic class label constraints to try to solve some problems of this type of algorithm in face recognition. By designing the constraints of atomic class labels, the dictionary has stronger discriminative performance, and then the sparse representation of test samples and dictionary learning are designed to integrate the face recognition system model, and a specific dictionary is learned for each test sample to improve face recogniti...

AI Technical Summary

Problems solved by technology

[0002] The constraint-based dictionary learning theory has opened up a new research direction for exploring the features in the data and enhancing the discrimination of the dictionary. Learning to deal with a series of unresolved problems separately

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