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Single sample face recognition method based on semi-supervised block joint regression

A semi-supervised, sample person technology, applied in the field of face recognition, can solve problems such as performance degradation and inability to work

Active Publication Date: 2017-04-26
HOHAI UNIV
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Problems solved by technology

[0003] Technically speaking, single-sample face recognition further highlights the contradiction between the small number of training samples and the high dimensionality of image data, which makes many existing face recognition technologies either experience serious performance degradation or fail to work at all, so they are rejected. Recognized as one of the most challenging research topics

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  • Single sample face recognition method based on semi-supervised block joint regression
  • Single sample face recognition method based on semi-supervised block joint regression
  • Single sample face recognition method based on semi-supervised block joint regression

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Embodiment Construction

[0060] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0061] The face change information is actually contained in the unlabeled test face image. Therefore, fully considering the unlabeled test face image in single-sample face recognition can effectively learn the face image change, thereby avoiding the general learning method. Dependencies and limitations of the training sample set. Based on this idea, the present invention proposes a single-sample face recognition method based on semi-supervised sub-block joint regression.

[0062] Such as figure 1 As shown, the present invention is based on the single-sample face recognition method of semi-supervised sub-block joint regression, comprising the following steps:

[0063] ...

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Abstract

The invention discloses a single sample face recognition method based on semi-supervised block joint regression. The method comprises steps: a face is firstly divided into multiple blocks; a semi-supervised-based block joint regression model is then brought forward to make full use of face images with labels and with no labels to learn various change information of the face images, isometric constraints with each class of the face images with no labels in class label coordinate space are added to avoid influenced model discrimination by the data with no labels, a non-strict augmented Lagrange multiplier is used for acquiring a mapping matrix corresponding to each block; on the basis, regression classification on a test image block can be realized through the mapping matrix; and finally, all test image blocks are voted to finally determine a classification result. The single sample face recognition method has good robustness towards expressions, illumination variation and occlusion, and the recognition precision is high.

Description

technical field [0001] The invention relates to a single-sample face recognition method, in particular to a single-sample face recognition method based on semi-supervised sub-block joint regression in which each object to be recognized has only one training image, and belongs to the technical field of face recognition. Background technique [0002] In the past forty years, face recognition technology has made remarkable progress, and face recognition technology under controllable conditions has basically matured. However, under uncontrolled conditions, due to the influence of factors such as illumination, expression, posture, noise, occlusion, etc., face recognition is still a very challenging research problem. The most direct way to solve these problems is to increase the training samples, but in many practical applications such as ID card identification, passport identification, judicial confirmation, access control, etc., usually only one training sample can be obtained. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/172
Inventor 刘凡许峰
Owner HOHAI UNIV
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