Research and implementation of embedded face recognition based on Adaboost and PCA

WANG Hong-yan; MENG Xiang-yin; ZHAO Yang; TAO Tao

doi:10.13873/j.1000-9787(2017)06-0059-04

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Research and implementation of embedded face recognition based on Adaboost and PCA

更新时间：2025-09-11

- Research and implementation of embedded face recognition based on Adaboost and PCA
- Vol. 36, Issue 6, Pages: 59-62(2017)
- 作者机构：
  
  西南交通大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)06-0059-04
  CLC： TP391.41
- Published：2017
- 稿件说明：
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[1]王鸿雁,孟祥印,赵阳,陶涛.基于Adaboost和PCA的嵌入式人脸识别方法[J].传感器与微系统,2017,36(06):59-62.

WANG Hong-yan, MENG Xiang-yin, ZHAO Yang, et al. Research and implementation of embedded face recognition based on Adaboost and PCA[J]. 2017, 36(6): 59-62.
[1]王鸿雁,孟祥印,赵阳,陶涛.基于Adaboost和PCA的嵌入式人脸识别方法[J].传感器与微系统,2017,36(06):59-62. DOI： 10.13873/j.1000-9787(2017)06-0059-04.

WANG Hong-yan, MENG Xiang-yin, ZHAO Yang, et al. Research and implementation of embedded face recognition based on Adaboost and PCA[J]. 2017, 36(6): 59-62. DOI： 10.13873/j.1000-9787(2017)06-0059-04.

摘要

针对传统的Adaboost算法和主成分分析（PCA）算法用于人脸识别时在环境与姿态等非约束性条件下识别率大大降低以及要求训练样本符合高斯分布的缺陷

提出了一种融合Adaboost和PCA的与或关联决策方法。一方面

在需要安防模式时开启或决策

拒绝近似全部负样本的请求

最大限度保证识别的正确率;另一方面

在需要访客模式时开启与决策

以减少正样本的丢失。在Samsung 2440嵌入式Linux平台上采用该方法进行人脸检测时

基于2种决策方法

分别满足各自阈值。实验结果表明:该方法在嵌入式平台运行稳定

适合推广于智能家居控制与楼宇自动化控制。

Abstract

Aiming at defects of traditional Adaboost algorithm and principal component analysis（ PCA） algorithm for face recognition in environment and pose and other nonbinding condition recognition rate is greatly reduced and the requirements of training samples in accordance with the Gauss distribution

a fusion of Adaboost and PCA and or related decision-making method is proposed. On the one hand

when the security mode is needed

the request of the whole negative sample is rejected

and the correct rate of recognition is guaranteed. On the other hand

face recognition in the need to open the visitor mode and decision-making

to reduce the loss of positive samples. On Sumsung 2440 embedded Linux platform

using the method of face detection

based on two decision-making methods

respectively meet the respective threshold. Experimental results show that the method works on embedded platform stably

suitable for promotion in intelligent home control and building automation and control.

关键词

Keywords

references

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一种新的人脸图像去噪算法 [J]. 沈千里,陈晓,支亚京,蔡婷婷. 传感器与微系统 . 2015(11)

嵌入式人脸识别系统的开发 [J]. 赵宏,梁喜军,杨玉东,周春龙. 长安大学学报(自然科学版) . 2015(S1)

基于PCA的人脸识别系统的研究与实现 [J]. 孔令钊,唐文静. 计算机仿真 . 2012(06)

基于ARM9的人脸识别门禁系统设计 [D]. 刘耀庭. 南京航空航天大学 2011

AdaBoost-based face detection for embedded systems [J] . Ming Yang,James Crenshaw,Bruce Augustine,Russell Mareachen,Ying Wu. Computer Vision and Image Understanding . 2010 (11)

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