LBP and deep learning-based human face identification method

A face recognition and deep learning technology, applied in the field of face recognition based on LBP and deep learning, can solve the problem of insufficient local binary pattern feature extraction, so as to improve recognizability, improve utilization, and reduce learning. Effect

Inactive Publication Date: 2018-02-23
NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of insufficient feature extraction of traditional local binary patterns (LBP) and classifier fitting, the present invention provides a face recognition method based on LBP and deep learning that is beneficial to extract more useful texture information of face images

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  • LBP and deep learning-based human face identification method
  • LBP and deep learning-based human face identification method
  • LBP and deep learning-based human face identification method

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Embodiment Construction

[0024] The present invention is described in detail below with reference to accompanying drawing and embodiment:

[0025] A face recognition method based on LBP and deep learning, comprising the steps of:

[0026] The face image training sample is divided into blocks, and the classic uniform block method is adopted. If the image block is too large, such as 4×4, it will contain some interference and noise to affect the block effect. If the image block is too small, such as 16× 16. It will increase the threshold of adjacent areas and affect the effect of feature expression, so this paper divides the samples into 8×8 blocks.

[0027] Use a well-shaped LBP to extract the LBP histogram of each sub-block, and connect the histograms of each sub-block end to end to form a sample overall histogram;

[0028] Input the LBP feature of the training sample to the visual layer of DBN, train the first RBM, use the output of the first RBM as the input of the second RBM to train the second RBM...

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Abstract

The invention relates to a #-shaped LBP and deep learning combination-based human face identification method, and belongs to the technical field of artificial intelligence. For the problems of insufficient feature extraction and classifier fitting of a conventional LBP, a human face identification method based on a #-shaped LBP and deep learning of a local texture feature is proposed. By utilizingan improved LBP algorithm, the local texture feature of a human face image is extracted, and an LBP histogram is established; then a deep belief network-based deep learning framework is established,the LBP histogram is input to a deep belief network, the network is trained by adopting an unsupervised layer-by-layer training method and a supervised BP algorithm, self-learning and self-optimization of the network are realized, and network parameters are obtained; and finally, the human face image is classified and identified by utilizing the DBN. According to the method, the distinctive texture feature of the human face image can be extracted; the superiority of the algorithm in identification rate is verified; and the robustness is relatively strong.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular, relates to a face recognition method based on LBP and deep learning. Background technique [0002] Face recognition is an identification technology based on facial features, which has attracted great attention from various fields. In recent years, methods based on deep learning ideas have been greatly developed and applied, and breakthrough results have been achieved in research fields such as computer vision and object recognition, providing algorithmic support for the development of computer science in the direction of intelligence. At present, the method of deep learning has been introduced into the related research field of face recognition by more and more researchers, and many satisfactory results have been achieved. In 2006, Hinton proposed the DBN algorithm, which is a representative deep learning method. Its advantage is that it has a strong abilit...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06V40/168G06F18/2155
Inventor 任红格史涛王玮李福进赵传松杜建宫海洋
Owner NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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