Human face identification method based on manifold learning

A technology of face recognition and manifold learning, which is applied in the field of face recognition based on manifold learning, can solve the problems of not being able to discover the essential structure of high-dimensional face data, and not making good use of the local geometric information of the data, to achieve The effect of high face recognition rate

Inactive Publication Date: 2007-11-28
SHANGHAI JIAO TONG UNIV
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AI Technical Summary

Problems solved by technology

Traditional dimensionality reduction methods such as principal component analysis cannot discover the essential structure of high-dimensional face data
Principal component analysis deals with the global structure of the data, and does not make good use of the local geometric information of the data

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  • Human face identification method based on manifold learning
  • Human face identification method based on manifold learning
  • Human face identification method based on manifold learning

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Embodiment Construction

[0017] The embodiments of the present invention are described in detail below: the present embodiment is implemented under the premise of the technical solution of the present invention, and detailed implementation and specific operation process are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0018] The embodiment adopts a public face database: ORL database. The ORL database contains 40 individuals with 10 images per person. The size of each image is 40×40. First stack the rows of the face image into a 1600-dimensional long vector, and then all the data form a 1600×400 matrix X=[x 1 ,L,x 400 ]. For each person, 5 samples are randomly selected for training and the rest are used for recognition. For each given number of training samples, 20 sets of random training-recognition sample sets are generated, and the average recognition rate is calculated on this basis. The present invention first finds 4 s...

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Abstract

The invention relates to a face identification method based on the manifold learning, which comprises the following steps: offering partial category relation saving the face data from the data partial structure; converting the optimum partial of every point to the total optimum with the arrangement technique; getting the data projection matrix from higher dimensional to lower dimensional by proceeding characterized dissociation for arrangement matrix, the original training sample and transposed product; mapping the identifying face image to the lower dimensional space; classifying with the nearest neighbor method in the lower dimensional space. The invention provides the higher face identification ratio, which provides higher identification ratio than the identification with principal component analytical method.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a face recognition method based on manifold learning. Background technique [0002] With the advent of the information age, data sets have undergone significant changes compared to the past, and their main characteristics can be summarized as: high data volume, high dimensionality, high data growth rate, unstructured, and cannot be processed by human perception alone. On the one hand, the amount of knowledge that can be obtained is greater; on the other hand, it still faces the difficult problem of not being able to find the required knowledge reasonably and effectively from the data. The representation methods of many problems make the information very sparse, and how to densify the information is a difficult problem. [0003] Through document retrieval to prior art, it is found that M.Turk et al. in "Journal of Cognitive Neuroscience" Vol.3, No.1, 1991, 71...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06K9/00275G06V40/169
Inventor 张田昊杨杰杜春华袁泉吴证
Owner SHANGHAI JIAO TONG UNIV
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