The Ethnic Facial Research Based on Haarlike Features

DUAN Xiao-dong; WANG Cun-rui; LI Zhi-jie; ZHANG Qing-ling

Chinese

您当前的位置：

首页 >

文章列表页 >

The Ethnic Facial Research Based on Haarlike Features

更新时间：2026-09-04

- The Ethnic Facial Research Based on Haarlike Features
- Vol. 28, Issue 7, Pages: 17-20(2011)
- 作者机构：
  
  1. 1 大连民族学院非线性信息技术研究所,辽宁,大连,116600
  2. 2 东北大学系统科学研究所,辽宁,沈阳,110004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
段晓东, 王存睿, 李志洁, 张庆灵. 基于Haarlike的人脸民族特征分析[J]. 微电子学与计算机, 2011, 28(7): 17-20.

DUAN Xiao-dong, WANG Cun-rui, LI Zhi-jie, et al. The Ethnic Facial Research Based on Haarlike Features[J]. 2011, 28(7): 17-20.
段晓东, 王存睿, 李志洁, 张庆灵. 基于Haarlike的人脸民族特征分析[J]. 微电子学与计算机, 2011, 28(7): 17-20. DOI：

DUAN Xiao-dong, WANG Cun-rui, LI Zhi-jie, et al. The Ethnic Facial Research Based on Haarlike Features[J]. 2011, 28(7): 17-20. DOI：

摘要

文中利用Haarlike模板来对不同族群的人脸进行特征提取.利用Haarlike模板生成算法筛选出用于分析人脸特征的Haarlike模板

对构建的人脸数据库进行特征提取

利用C5.0、C%RTree、BPNN和SVM对训练样本进行学习

并对测试集进行分类和预测

其对维族、藏族、壮族的识别精度分别为80%、74%、88%.为人脸的民族特征识别提供了一种高效快速的方法.

Abstract

This paper presents a way using Haarlike template to extract face feature on different ethnic groups.Using the Haarlike template generation algorithm searches the templates for the analysis of facial features

completed feature extraction on our multi-ethnic face database

uses C5.0

C%R Tree、BP NN and SVM classifier to study the training samples

classifies and predicts the test dataset.The identification accuracy on the ethnic of Uighur

Tibetan

Zhuang is 8O%、74%、88% Separately.It also provides a rapid and efficient method for the facial Ethnic feature identifieation.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of ultra-low power real-time clock circuits

A review of large model development: technological evolution from BERT to Deepseek

High gain stability high temperature operational amplifier based on reverse body bias

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Aerial small object detection algorithm based on improved RT-DETR

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰