摘要
针对基于主成分分析(principal component analysis,PCA)方法在特征提取过程中丢弃高阶统计信息的缺陷,提出了一种基于图像重构的特征补偿人脸识别算法。首先利用白化PCA方法提取原始图像特征,对图像进行重构并计算残差图像;然后对残差图像进行白化PCA特征提取,并将其作为第一次提取特征的有效补偿以得到新的特征;最后用最近邻分类器进行识别分类。在ORL、YALE、XM2VTS和AR人脸数据库上的实验结果验证了算法的有效性。
According to the defect of PCA method which discards high-order statistical information in the process of feature ex- traction, this paper proposed a new feature compensation method for face recognition based on image reconstruction. Firstly, it extracted features from the original images using whitening PCA method, and it reconstructed the images and calculated the re- sidual images. Secondly,it extracted features from the residual images using whitening PCA method, these features were effec- tive compensation for previously obtained features to get the new features. Finally ,it used nearest neighbor classifier for classifi- cation. Experiments on ORL, YALE, XM2VTS and AR face databases demonstrate the effectiveness of the proposed algorithm.
出处
《计算机应用研究》
CSCD
北大核心
2015年第9期2853-2856,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61103128)
关键词
人脸识别
主成分分析
图像重构
特征提取
特征补偿
face recognition
principal component analysis （PCA）
image reconstruction
feature extraction
feature com- pensation
