人脸识别是人机接口和生物信息领域研究的重要方面，得到广泛的关注，人脸特征提取是其重要环节之一。为了克服人脸光照和表情变化对特征提取的影响，提出在小波包分解后的多分辨率下利用(2D)2PCA提取人脸特征进行识别的方法，主要创新包括：（1）以小波包分解所有节点图像为研究对象；（2）提出以识别率来选取“成功”节点；（3）提出一种融合节点图像的方法。首先通过二层小波包分解获取节点图像，采用(2D)2PCA方法提取所有节点图像的特征矩阵，并利用最邻近分类器获取其识别率，然后在选取“成功”节点图像的基础上，构建了一个融合方法进行人脸识别。用CMU PIE和Yale 库中的样本进行对比测试，结果表明本方法的高效性，同时也说明融合多分辨率下的节点图像能有效提高识别率。

Face recognition has aroused great concern for decades since it serves as a significant part in the fields of human-machine interaction as well as bioinformatics. Facial feature extraction is one of the key steps in face recognition system However, this step is characterized as being easily influenced by variations in face images such as illumination condition and expressions. In order to address this problem, a method that utilizes（2D)2PCA to extract facial features on the sub-bands obtained via wavelet packet decomposition（WPD) is proposed. There are three contributions:（1) take all multi-resolution sub-bands as research objects;（2) choose ‘successful’ sub-bands based on recognition rates;（3) propose a sub-band fusion method. Firstly, sub-bands are acquired by two-level WPD, then the feature matrixes of all sub-bands are calculated by（2D)2PCA, and further used to obtain recognition rates with the nearest neighborhood classifier. Thirdly, ‘successful’ sub-bands are chosen based on their recognition rates and fused to complete the task of face recognition. Finally, intra and extra experimental comparisons using samples of CMU PIE and Yale indicate that the proposed method gain satisfactory results and fusing sub-bands on multi-resolution can improve recognition performance.