Face Recognition with Features Extraction Based on Particle Swarm Optimization

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Face Recognition with Features Extraction Based on Particle Swarm Optimization

更新时间：2025-07-09

- Face Recognition with Features Extraction Based on Particle Swarm Optimization
- Vol. 44, Issue 4, Pages: 48-51+118(2010)
- 作者机构：
  
  西安电子科技大学综合业务网理论及关键技术国家重点实验室,西安,710071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Online First：10 April 2010，
  
  Published：2010
- 稿件说明：
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Face Recognition with Features Extraction Based on Particle Swarm Optimization[J]. 2010, 44(4): 48-51+118.
DOI：

Face Recognition with Features Extraction Based on Particle Swarm Optimization[J]. 2010, 44(4): 48-51+118. DOI：

摘要

针对如何提高人脸图像识别率问题

提出了利用粒子群优化(PSO)的人脸特征提取识别算法.采用小波变换和张量主成分分析(PCA)方法对人脸图像进行特征提取

利用PSO对提取的特征进行加权处理

根据特征的每一维元素的聚类正确率进行优化选择

从而达到对人脸提取关键性特征的目的.实验结果表明

所提算法能减小光照、表情和姿态变化的影响

在英国曼彻斯特科技大学人脸数据库上的识别率比张量PCA方法提高了12.75%.

Abstract

A face recognition algorithm with optimal features extraction based on particle swarm optimization(PSO)is proposed to enhance the recognition rate. Features of each face image are extracted by using the wavelet transformation and the tensor principal component analysis(PCA)algorithm. Weights of the features' elements are then determined using PSO according to the right clustering rate of each element

so that the object to extract the key features of the faces can be realized. Experimental results on the UMIST database show that the impact of changes in expression

light and posture can be reduced by the proposed algorithm

and that the recognition ratio is increased by 12.75% compared with tensor PCA.

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references

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