Based on gb(2d) 2 Face recognition method of pcanet deep convolution model

An identity recognition and deep convolution technology, applied in character and pattern recognition, neural learning methods, biological neural network models, etc. Effect

Active Publication Date: 2019-07-16
慧镕电子系统工程股份有限公司
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Problems solved by technology

[0006] The present invention aims at the problems existing in the above-mentioned face recognition, and proposes a method based on GB(2D) 2 The face recognition method of the PCANet deep convolution model not only absorbs the advantages of the depth model and Gabor filtering, but also can extract more abstract features in the data, and is robust to factors such as illumination, expression, and occlusion, and overcomes the convolution Disadvantages of time-consuming neural network and large number of labels

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  • Based on gb(2d) <sup>2</sup> Face recognition method of pcanet deep convolution model
  • Based on gb(2d) <sup>2</sup> Face recognition method of pcanet deep convolution model
  • Based on gb(2d) <sup>2</sup> Face recognition method of pcanet deep convolution model

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[0034] In order to better illustrate the purpose, specific steps and characteristics of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings, taking the AR face library [5] as an example:

[0035] The present invention proposes a method based on GB(2D) 2 Face recognition method of PCANet deep convolution model, where GB(2D) 2 PCANet depth convolution model such as figure 1 shown. GB(2D) 2 PCANet consists of two feature extraction layers and a nonlinear output layer. The convolution filter of the feature extraction layer is composed of Gabor and (2D) 2 PCA learning is used to convolve the original input image to extract features, and the nonlinear output layer includes binary hash and local histogram calculation operations, which are used to further calculate the final features.

[0036] The present invention proposes a method based on GB(2D) 2 Face recognition method of PCANet deep convolution...

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Abstract

The invention discloses a GB(2D) based 2 Face recognition method for PCANet deep convolutional model. The model training method includes the following steps: sending preprocessed face samples to the first feature extraction layer in turn, scanning multiple sub-blocks from the acquired Gabor feature image and removing the mean value, using (2D) 2 PCA extracts the optimal projection axis, and convolves with the original sample of the training set to obtain the first layer feature map; send the first layer feature map to the second feature extraction layer, repeat the previous steps to obtain the second layer feature map; The output feature map is optimized, and the local area histogram is calculated and stitched as the final feature; the final feature is sent to the linear SVM classifier to obtain an optimized face recognition model. The invention can automatically learn effective feature expression, not only has good locality, but also has good robustness to illumination, expression and noise, etc., and improves the recognition performance of face identity.

Description

[0001] Technical field: [0002] The invention belongs to the field of machine vision, in particular to a GB(2D) based 2 Face recognition method for PCANet deep convolutional model. [0003] Background technique: [0004] Face recognition technology is a technology that uses computers to analyze face videos or images, extract facial features from them, and identify identities through these features. [0005] At present, face recognition technology is developing rapidly, and a large number of research results have been obtained. Common face recognition algorithms can be divided into several categories: face recognition based on geometric features, face recognition based on subspace analysis, face recognition based on elastic matching, and face recognition based on hidden Markov model. Identity recognition, face recognition based on neural network and face recognition based on 3D. For example, Takatsugu et al. [1] used an elastic matching method based on dynamic link structure...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V40/168G06F18/214
Inventor 蒋敏鹿茹茹孔军孙林胡珂杰王莉
Owner 慧镕电子系统工程股份有限公司
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