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Sparse representation face identification method based on constrained sampling and shape feature

A shape feature and face recognition technology, applied in the field of face recognition, can solve the problems of high image registration requirements and achieve high face recognition rate

Inactive Publication Date: 2012-02-29
TSINGHUA UNIV
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Problems solved by technology

[0021] This method is robust to changes in facial illumination and expression, but has high requirements for image registration

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  • Sparse representation face identification method based on constrained sampling and shape feature
  • Sparse representation face identification method based on constrained sampling and shape feature
  • Sparse representation face identification method based on constrained sampling and shape feature

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Embodiment Construction

[0058] The sparse representation face recognition method combined with shape features proposed by the present invention is described in detail in conjunction with the accompanying drawings and embodiments as follows, including the following steps:

[0059] 1) Extract the texture features of all the images in the training set to obtain the texture feature vectors of the images in the training set, and arrange all the texture feature vectors in the training set to form a texture feature matrix A 1 , using one or more rows in the texture feature matrix as a category of the training set, and one category corresponds to multiple face images of a person in the training set;

[0060] 2) Extract the shape features of all images in the training set to obtain the shape feature vectors of the images in the training set, and arrange all the shape feature vectors in the training set to form a shape feature matrix A 2 , using one or more rows in the shape feature matrix as a category of the...

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Abstract

The invention relates to a sparse representation face identification method based on constrained sampling and shape features, belonging to the field of image processing. The method comprises the following steps: extracting the textural features and shape features of face images in a training set based on constrained regions to obtain a textural feature matrix and a shape feature matrix, wherein each category of the textural feature matrix and the shape feature matrix respectively corresponds to the multiple face images of one person in the training set; and comparing each category in the training set with the obtained textural feature vector and shape feature vector of the face image to be identified, and taking the category in the training set corresponding to the maximum comprehensive similarity value as the identification result of the person to be identified. The method has higher face identification rate.

Description

technical field [0001] The invention belongs to the technical fields of image processing, computer vision and pattern recognition, and in particular relates to a face recognition method. Background technique [0002] Biometric feature recognition technology is an effective technology for identification, and the fastest growing recently is face recognition technology and biometric feature recognition technology integrated with face recognition technology. [0003] Currently existing face recognition methods mainly recognize the entire face, and among many recognition methods, principal component analysis (PCA-Principal Component Analysis), elastic matching, neural network, geometric features and other methods are mainly used. [0004] At the same time, the difficulty of face recognition lies in: [0005] (1) Face plastic deformation caused by expression [0006] (2) Face diversity caused by posture [0007] (3) Face changes caused by age [0008] (4) The multiplicity of f...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/66
Inventor 苏光大王晶陈健生熊英刘炯鑫任小龙
Owner TSINGHUA UNIV
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