针对现有人脸识别方法在光照变化、表情变化及噪声干扰等情况下识别率下降的问题，本文将主成分分析 (PCA)，图像的小波包分解 (WPD)和稀疏表示分类 (SRC)等算法结合起来进行研究分析，提出了一种融合小波包细节子图及稀疏表示 (FW-SRC)的人脸识别方法。该方法首先将图像小波包分解以后的子图像进行加权融合，对融合后的图像进行特征提取并构造特征空间，然后用样本在特征空间上的投影集构造稀疏字典，最后通过对人脸图像的稀疏表示实现分类识别。采用 Yale B、AR和 CMU PIE人脸库分别进行了光照、表情及噪声鲁棒性的测试，实验结果表明本文方法不仅提高了人脸识别率，而且在光照强度变化、表情变化以及噪声干扰的情况下具有良好的识别性能。

Under the influence of factors such as the variations in illumination, facial expressions and noises, the accuracy of many current face recognition methods is not very satisfactory. A face recognition method named FW-SRC isproposed which is based on the fusion of wavelet packet sub-images (FW) and Sparse Representation-based Classification (SRC) via the combining research and analysis of Principal Component Analysis (PCA), wavelet packet decomposition (WPD) of images and SRC. The proposed method extracted features from the weighted fusion images of wavelet packet sub-images and then constructed the feature space by the weighted fusion images, and constructed sparse dictionary by projection of samples on the feature space, at last classified the faces by SRC. Experimental results on the Yale B, AR and CMU PIE face databases for the robustness test show that the proposed method improves the accuracy for face recognition and it has a good performance.