结合小波低频子带的主成分分析方法

Principal Component Analysis Combined with Wavelet Low-Frequency Band

针对单样本人脸识别问题,提出了一种结合小波低频子带的主成分分析方法.为了加强单样本图像的分类信息,该方法将原训练样本与其小波低频子带重构图相结合,然后对结合的训练样本进行主成分分析.在ORL人脸库上的实验结果表明,当训练集中每个人只有一幅人脸样本图像时,文中方法的识别率比标准特征脸法高3.6%,而所使用的特征脸个数减少14.8%.

This paper proposes a principal component analysis method combined with wavelet low-frequency band to deal with the face recognition with single training sample. To enhance the classification information of a single sample image, the method combines the original training image with its reconstructed image based on wavelet low-frequency band, and performs the principal component analysis on the joined version of the training image. Experimental results on the ORL database show that, when each person has only one training sample, the proposed method achieves 3.6% higher recognition accuracy and uses 14. 8% fewer eigenfaces than the standard eigenface algorithm.