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A Facial Age Estimation Method Based on Deep Classification Network

A deep classification and network technology, applied in the fields of computer vision and human-computer interaction, can solve the problems of long training time and high hardware configuration

Active Publication Date: 2019-12-10
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage is that it needs to be calculated on the GPU server, the hardware configuration is high, and the training time is too long

Method used

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  • A Facial Age Estimation Method Based on Deep Classification Network
  • A Facial Age Estimation Method Based on Deep Classification Network
  • A Facial Age Estimation Method Based on Deep Classification Network

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Embodiment Construction

[0065] Implementation language: Matlab, C / C++

[0066] Hardware platform: Intel core2 E7400+4G DDR RAM

[0067] Software platform: Matlab2015a, VisualStdio2010

[0068] Adopt the method of the present invention, first utilize SeatFace toolkit to extract the feature point of facial image on VisualStdio2010 platform, and record the feature point position corresponding to each image. Then according to the patent content, use C++ or matlab programming to realize the algorithm, extract facial features and return to the age category layer by layer. Finally, according to the learned deep classification network, use the above code to estimate the corresponding age of the estimated sample.

[0069] The method is a facial age estimation method based on a deep classification network, comprising the following steps:

[0070] Step 1: Collect N face images of different people with different ages, and calibrate the corresponding actual age;

[0071] Step 2: Use SeataFace to track facial ...

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Abstract

This patent proposes an age estimation method based on a deep classification network, which belongs to the field of computer vision and machine learning. The main idea of ​​this method is to establish a mapping relationship between input facial image features and age through a deep classification network. First, normalize the facial image and extract facial features; then, build a 5-layer deep classification model to fit the mapping relationship between input image features and age; after that, optimize the parameters of the deep classification model by using the gradient descent method; Finally, for the face image to be estimated, use the learned depth model to estimate the age.

Description

technical field [0001] The invention belongs to the field of computer vision technology, relates to facial age estimation technology, and is mainly applied to the fields of age-based login control, age-differentiated advertisement, and age-related human-computer interaction technology. Background technique [0002] Facial age estimation technology refers to the technology of automatically estimating the age of the human body after analyzing the facial features of the human face through computer algorithms. Usually, a computer collects a face image (photo) through a camera, extracts and analyzes facial features, and automatically estimates the age corresponding to the image. Since this technology has very wide applications in age-related human interaction, age-based login control, and age-differentiated advertising, it has attracted extensive interest and attention from scholars in the field of computer vision. The existing facial age estimation algorithms can be divided int...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/084G06V40/178G06V40/172
Inventor 潘力立
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA