Individual identity identification method based on deep learning

A technology of identity recognition and deep learning, applied in the field of individual identity recognition based on deep learning, can solve problems such as incompetence in complex large-scale data, poor generalization of measurement matrix, etc., to overcome the problem of individual identity recognition, strong generalization, The effect of great practical value

Inactive Publication Date: 2016-10-12
INST OF AUTOMATION CHINESE ACAD OF SCI
View PDF7 Cites 40 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

So far, the artificially designed features and metric matrices have poor genera

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Individual identity identification method based on deep learning
  • Individual identity identification method based on deep learning
  • Individual identity identification method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0031] The invention discloses an individual identification method based on deep learning. The method uses a non-shared weight dual-channel convolutional neural network to construct a matching model based on a convolutional neural network. The model includes two features: feature extraction and distance measurement. functional module. The entire technical solution process of the individual identification method of the present invention is as follows: figure 1 shown. This method specifically includes two processes of training and testing. The technical details involved in the invention will be described in detail below.

[0032] Step S1, training process:

[0033] Step S11: Use the marked individual data for pairing to...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

An individual identity identification method based on deep learning comprises the steps of: utilizing individual data with marked identities to carry out pairing, establishing positive and negative training samples, utilizing the training samples to train an established dual-channel convolution neural network model until the neural network model is convergent, and obtaining neural network model parameters after the training; and utilizing the trained neural network model and related parameters to carry out matching between individual images to be identified and registered individual images, determining the identities of the individual images to be identified according to the magnitude ordering of matching similarities, and outputting a result. According to the invention, characteristic extraction and distance measuring are unified in one one-to-end network, and the global optimization of the whole body is realized; a deep network is utilized to learn characteristic and measuring matrixes, the generalization is higher, and the individual identity identification problem under complex large-scale data is overcome; in addition, the real-time speed is reached, and the practical value is higher.

Description

technical field [0001] The present invention relates to the field of video surveillance and pattern recognition, in particular to an individual identification method based on deep learning, which mainly solves the problem of individual identification based on different viewing angles or under cameras. Background technique [0002] At present, most of the individual identification problems based on different viewing angles or cameras follow the traditional solution framework, that is, first perform feature extraction on two individual pictures under different viewing angles, and then establish a feature measurement matrix through sample learning. The features are matched, the distance is calculated, and then the distance is used to judge whether the two individual pictures belong to the same person. The effectiveness of the framework depends on the strength of the extracted feature representation and metric matrix. So far, the artificially designed features and metric matric...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06N3/06
CPCG06N3/06G06F18/214
Inventor 黄凯奇陈威华康运锋
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap