Multi-band light source face recognition method based on convolutional neural network

A convolutional neural network, face recognition technology, applied in the direction of biological neural network model, neural architecture, character and pattern recognition, etc.

Pending Publication Date: 2021-05-14
GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

[0010] The technical problem to be solved in the present invention is to provide a multi-band light source face recognition method based on convolutional neural network, which solves the main problems faced by face recognition under complex illumination changes, and further improves the recognition performance

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Embodiment Construction

[0034] The present invention will be further described in detail below in combination with specific embodiments.

[0035] A multi-band light source face recognition method based on a convolutional neural network, specifically comprising the following steps.

[0036] A. Collect multi-band light source images of visible light a, near-infrared b, mid-infrared c, far-infrared d and thermal infrared e as the data source for face recognition.

[0037] After the images of light sources in different bands are acquired, the images need to be preprocessed, and the processing steps include light compensation, grayscale correction, and noise filtering.

[0038] B. Based on different types of convolutional neural networks, extract face feature expression vectors under different light source data;

[0039] Visible light: Fa=Fa1+Fa2+...+Fan;

[0040] Near infrared: Fb=Fb1+Fb2+...+Fbn;

[0041] Mid-infrared: Fc=Fc1+Fc2+...+Fcn;

[0042] Far infrared: Fd=Fd1+Fd2+...+Fdn;

[0043] Thermal ...

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Abstract

The invention discloses a multi-band light source face recognition method based on a convolutional neural network, and the method comprises the steps of collecting face data under different band light sources, extracting related feature vectors based on different types of convolutional neural networks, carrying out the dimensionality reduction and feature selection of the feature vectors based on an arcface method, and finally, using a nearest neighbor classifier to obtain a face recognition result, so that the main problems existing in face recognition under complex illumination changes are solved, face image information of different light sources can be fused, and the recognition precision is improved. More accurate face recognition can be realized in an invisible light environment; in addition, the face is expressed by the low-dimension feature vector containing richer information, the recognition speed under the condition of extremely rich information can be improved, and the face recognition capability is further improved.

Description

technical field [0001] The invention relates to the fields of pattern recognition and computer vision, in particular to a face recognition method based on a convolutional neural network. Background technique [0002] Among the information obtained by the human perception system, visual information accounts for about 80%-85%. Applications related to images and videos are increasingly prominent in the daily life of citizens. Image processing is not only a challenging theoretical research direction in the field of science, but also an important application technology in the field of engineering. [0003] Face recognition technology refers to the use of computer technology for analysis and comparison to identify human faces, which belongs to biometric identification technology, and is to distinguish individual organisms by their biological characteristics (generally referring to people). Face recognition is a popular computer technology research field, including face tracking ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/20G06K9/62G06N3/04
CPCG06N3/04G06V40/168G06V40/172G06V10/143G06N3/045G06F18/2413G06F18/251
Inventor 代晓丰陈泽涛郝方舟黄志滔王增煜
Owner GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
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