摘要
提出一种改进的小波包融合+2DPCA方法,先对图像进行二层小波包分解,再选取最利于判别分类的4幅高频子图进行融合,将融合子图与低频子图分别进行2DPCA降维和特征提取,最后进行决策级融合,得到识别结果。在Yale和JAFFE标准人脸库上的实验结果表明,该改进方法能有效提高识别率。
An improved method based on wavelet packets fusion and 2DPCA is proposed. Firstly, the original face image is decomposed by wavelet packets at two levels, and four most conducive high-frequency sub-images are selected and fused to improve the performance of classification. Then, 2DPCA is carried out in high-frequency and low-frequency sub-graphs separately. Finally, the decision level fusion is used to get the recognition result. Experimental results show that the proposed method is effective to face recognition with Yale and JAFFE databases.
出处
《计算机工程与应用》
CSCD
2014年第8期158-160,187,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.61170090)
中央高校基本科研业务费专项资金(No.2011-IV-058)
关键词
人脸识别
矩阵主成分分析
小波包
face recognition
2-Dimensional Principal Component Analysis （2DPCA）
wavelet packets
