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中文摘要：
提出一种基于人脸直观上镜像对称的算法——对称二维主成分分析，并成功应用于人脸识别．该算法引入镜像变换，根据奇偶分解原理，分别生成奇偶对称样本，再分别进行二维PCA变换，生成奇偶本征空间．根据选择性集成的思想，从奇偶本征空间挑选出更具有鉴别信息的本征向量构造人脸特征提取的本征空间．提取人脸图像的各奇偶对称的二维主成分特征进行识别．理论分析与实验证明，该算法既扩大样本容量，又提高识别率，同时该算法对光照变换有一定的不敏感性．
英文摘要：
An algorithm is proposed, called symmetrical two dimensional principal component analysis （S2DPCA）. It is based on the theory of function decomposition in algebra and mirror symmetry in geometry. Firstly, mirror transform is applied to images. Then, the images are decomposed into even and odd symmetrical images, and 2DPCA are performed on the even and odd images respectively. According to the idea of selective ensemble, the more discriminant eigenvectors of the even and odd image space are selected to construct the final eigenspace. Finally, the even and odd 2DPCA features are gotten by projecting samples onto the eigenspace. Both theoretical analysis and experimental results demonstrate that the algorithm can enlarge the number of training samples and raise the recognition rate .
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