Unlock instant, AI-driven research and patent intelligence for your innovation.

Facial age estimation method based on depth classification network

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

Active Publication Date: 2017-05-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF8 Cites 2 Cited by
  • 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

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
  • Facial age estimation method based on depth classification network
  • Facial age estimation method based on depth classification network
  • Facial age estimation method based on depth classification network

Examples

Experimental program
Comparison scheme
Effect test

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 ...

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

The invention provides an age estimation method based on a depth classification network, and belongs to the field of computer vision and machine learning. The main idea of the method is that the mapping relationship among input facial image features and ages is established through the depth classification network. The age estimation method comprises the following steps of: firstly, performing normalization of a facial image, and extracting facial features; then, establishing a five-layer depth classification model, and fitting the mapping relationship among input image features and ages; optimizing parameters of the depth classification model by utilizing a gradient descent method; and finally, for the facial image to be estimated, estimating the age by utilizing the well learned depth model.

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

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): G06K9/00G06N3/08
CPCG06N3/084G06V40/178G06V40/172
Inventor 潘力立
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA