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Iris sorting scheme based on kernel clustering

A classification method and kernel clustering technology, applied in the field of image processing

Active Publication Date: 2013-03-13
HOPE CLEAN ENERGY (GRP) CO LTD
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Problems solved by technology

[0004] At present, research in the field of iris recognition technology mainly focuses on iris positioning and matching recognition, and there is no research on classification and indexing of massive iris databases.

Method used

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  • Iris sorting scheme based on kernel clustering
  • Iris sorting scheme based on kernel clustering
  • Iris sorting scheme based on kernel clustering

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Embodiment Construction

[0053] Adopt the method of the present invention, use Matlab language to write iris recognition software, then use the CASIA2.0 iris database collected by the automation of Chinese Academy of Sciences as source data. Extract the iris pictures of 40 people from them, and attack them with Gaussian noise (choose 25 different sets of parameters), attack with productic noise (choose 25 different sets of parameters) and different angles (angle range 0° to 10°) ), a total of 2400 images were classified. The correct classification result is 98.3%, and the correct classification rates of each noise are 96.6%, 97.8% and 96.3%.

[0054] At the same time, 40 people were selected from the CASIA2.0 library, and each person had 9 iris pictures taken in different periods for classification training. The irises of 40 people were successfully divided into three categories with appropriate sizes, and the parameters of the support vector machine were obtained. In addition, select the same 40 peo...

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Abstract

An iris sorting scheme based on kernel clustering, belongs to the image processing techniques field, and relates to the technology of iris identification. When an iris database establishes clustering, firstly the iris normalization images are equally divided into an upper layer and a lower layer. The upper layer and the lower layer are analyzed by wavelet for three layers respectively, wavelet coefficients are chosen from seven channels to calculate energy and standard deviation; eigenvectors of iris database samples are obtained by which wavelet energy is divided by standard deviation of the upper layer and the lower layer; the eigenvectors is clustered based on kernel methods to obtain clustering results. When the iris waiting for testing is sorted, all eigenvectors in iris database are input into a support vector machine to exercise, and support vectors and discriminant function are obtained; the corresponding eigenvectors of iris samples waiting for testing are input into discriminant function to have a test, and the sorting results are obtained. The invention has the advantage of that clustering which the iris sample belongs to can be rapidly and correctly found in favor of improving the iris identification accuracy rate and the identification efficiency.

Description

technical field [0001] The invention belongs to the technical field of image processing, and mainly relates to iris identification technology in biometric identification. Background technique [0002] At present, the identification technology based on iris characteristics has penetrated into every aspect of daily life, and the sample size of iris database is also increasing. For example: the IRIS plan initiated by the British government requires at least one million travelers who frequently enter and leave the UK to collect irises for identification through video cameras, without the need to carry other identification certificates; The information is added to the citizen ID card. If the iris images of about 60 million citizens in the UK are successfully collected, the iris database will have a large number of samples. The population of our country is about 20 times that of the United Kingdom, and the number of people entering and exiting my country has reached more than 300...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 解梅郑韬
Owner HOPE CLEAN ENERGY (GRP) CO LTD
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