[1]苏士美,王燕,王明霞.基于加权小波分解的人脸识别算法研究[J].郑州大学学报(工学版),2014,35(01):5-9.[doi:10.3969/j.issn.1671-6833.2014.01.002]
 Susmette,Wang Yan,Wang Mingxia.Face Recognition Research Based on Weighted Wavelet Decomposition[J].Journal of Zhengzhou University (Engineering Science),2014,35(01):5-9.[doi:10.3969/j.issn.1671-6833.2014.01.002]
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基于加权小波分解的人脸识别算法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
35卷
期数:
2014年01期
页码:
5-9
栏目:
出版日期:
2014-02-28

文章信息/Info

Title:
Face Recognition Research Based on Weighted Wavelet Decomposition
作者:
 苏士美王燕王明霞
郑州大学电气工程学院,河南郑州,450001
Author(s):
Susmette;Wang Yan;Wang Mingxia
关键词:
小波分解PCA人脸识别离散小波变换
Keywords:
Wavelet decompositionPCAFace recognitionDiscrete wavelet transform
DOI:
10.3969/j.issn.1671-6833.2014.01.002
文献标志码:
A
摘要:
小波变换能有效地将图像分解成高频和低频信息.现有的人脸识别算法多数都是基于小波分解后的低频信息,没有充分利用高频信息.PCA是人脸识别中被广泛使用的一种算法,它具有实现简单、正面图像识别率高等优点,但PCA算法计算量大,且易受光照、表情变化等因素的影响.基于加权小波分解和PCA算法提出一种新的人脸识别算法,对小波二级分解后的低、高频子分量进行加权融合,以便充分利用人脸的细节信息,并分别给PCA前三个最大主分量赋予一个新权值,来弥补传统PCA算法对光照、表情变化敏感的缺点.实验结果表明提出的人脸识别算法在识别率和训练时间方面都得到了明显的改进.
Abstract:
Wavelet transform can decompose the image into high frequency and low frequency information effectively. Most of the existing face recognition algorithms are based on the low-frequency information after wavelet decomposition, and do not make full use of the high-frequency information. PCA is a widely used algorithm in face recognition. It has many advantages, such as simple implementation, high recognition rate of frontal image, etc. . However, PCA algorithm has a large amount of computation, and is easily affected by lighting, expression changes and other factors. A new face recognition algorithm based on weighted wavelet decomposition and PCA algorithm is proposed, which fuses the low-frequency and high-frequency sub-components after wavelet decomposition to make full use of face details, the first three maximum principal components of PCA are given a new weight to make up for the shortcomings of the traditional PCA algorithm which is sensitive to the changes of illumination and expression. Experimental results show that the proposed face recognition algorithm has been significantly improved in recognition rate and training time.

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