Constraint-based face identification method for single test sample

A test sample and face recognition technology, applied in the field of face recognition, can solve problems such as the influence of training sample noise, separate processing, and sparse representation of test samples

Active Publication Date: 2014-11-26
HANGZHOU YIYOU INFORMATION TECH CO LTD
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Problems solved by technology

[0002] The constraint-based dictionary learning theory has opened up a new research direction for exploring the features in t

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  • Constraint-based face identification method for single test sample
  • Constraint-based face identification method for single test sample
  • Constraint-based face identification method for single test sample

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Such as figure 1 As shown, in the current pattern classification algorithm based on dictionary learning, dictionary learning and sparse representation of test samples are processed separately. The present invention adds the sparse representation of test samples into the dictionary learning process, and designs a discriminative dictionary learning algorithm oriented to a single test sample in combination with atomic class label constraints, so that each test sample learns a specific dictionary, and at the same time uses the dictionary to perform a test on the test sample Classification. Aiming at the problem of high computational complexity of the algorithm, a two-stage discriminative dictionary learning framework and a face recognition system for a single test sample are proposed. Suppose the training sample set is N is the number of training samples, and n ...

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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...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 李争名徐勇
Owner HANGZHOU YIYOU INFORMATION TECH CO LTD
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