Partitioning human face recognition method based on weighted intensity PCNN model

A face recognition and block technology, applied in character and pattern recognition, instruments, computing, etc., can solve the problems of neglecting faces and insufficient refinement of image feature description.

Active Publication Date: 2017-05-24
谷德智能科技研究院(山西)有限公司
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Problems solved by technology

[0008] The present invention solves the problem that the image feature description in the existing face recognition method based on the PCNN model is not detailed enough and uses a set of parameters to process the entire face image indiscriminately, ignoring the different parts of the face, and provides A Face Recognition Method Based on Weighted Intensity PCNN Model

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  • Partitioning human face recognition method based on weighted intensity PCNN model
  • Partitioning human face recognition method based on weighted intensity PCNN model
  • Partitioning human face recognition method based on weighted intensity PCNN model

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Embodiment Construction

[0090] The block face recognition method based on the weighted strength PCNN model is realized by the following steps:

[0091] (1) Training stage

[0092] Construct or select an existing face library containing A person, B face images for each person, and a total of A*B face images; take the first N faces of each person in the face library, N≤B / 2, The face image is used as the training image, and each training image is horizontally divided into blocks according to the eyes, nose and mouth, and divided into upper, middle and lower block diagrams corresponding to the eyes, nose and mouth respectively. The three block images of each face image are input into the weighted intensity PCNN model, and each block will get a weighted intensity matrix, so that A*N*3 weighted intensity matrices will be obtained, which correspond to each person's Each block of the training images, and then calculate the average value of the N weighted intensity matrices corresponding to the upper, middle...

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Abstract

The invention relates to a human face recognition method based on a PCNN model, specifically a partitioning human face recognition method based on a weighted intensity PCNN model. The invention solves problems that a conventional human face recognition method based on the PCNN model is not fine in description of image features, carries out no-difference processing of the whole human face image through a group of parameters, and neglects the differences of all parts of a human face. On the basis of simplifying the PCNN model, the invention proposes the weighted intensity PCNN model, introduces the concepts of emission intensity of a spontaneous pulse, the emission intensity of a coupled pulse and the weighted intensity, and refines the output of the model. Meanwhile, the method employs the partitioning recognition during human face recognition. The method comprises the steps: enabling a human face image to be divided into blocks according to the difference of gray scale distribution of all parts of the human face image and the difference of local resolutions before recognition; adaptively setting the weight value of blocks according to the image blocks during recognition; finally enabling the recognition result of each block to be integrated in the recognition result of one human face image.

Description

technical field [0001] The invention relates to a face recognition method based on a PCNN model, in particular to a block face recognition method based on a weighted strength PCNN model. Background technique [0002] The pulse-coupled neural network was discovered by Eckhorn et al. when studying the synchronous pulse emission phenomenon of the brain visual cortex of cats, monkeys and other animals, and was improved by Johnson in 1993, and the standard pulse-coupled neural network (Pulse Coupled Neural Network) was proposed. Neural Network PCNN) model. In 1999, Lzhikevich strictly proved that the PCNN model is a network model closest to biological neurons from a mathematical point of view. Compared with the previous classical neural network, the PCNN model is a self-supervised and self-learning network, which can realize image processing without training, and the PCNN model has excellent comprehensive spatio-temporal summation characteristics, dynamic synchronous pulse emiss...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/16G06F18/2155
Inventor 邓红霞李海芳郭浩相洁曹锐李瀚杨晓峰
Owner 谷德智能科技研究院(山西)有限公司
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