基于对称核主成分分析的人脸识别

为了提高人脸识别技术的实用性，结合人脸镜像对称性和核主成分分析提出了一种新的人脸识别方法。首先利用小波变换压缩人脸图像数据，获取小波分解的低频分量，再通过镜像变换得到镜像偶对称图像和镜像奇对称图像，然后分别对奇偶对称图像进行核主成分分析提取奇偶特征，并且通过加权因子对奇偶特征进行融合，最后采用最近邻分类器分类。基于ORL人脸数据库的实验结果表明：该算法增大了样本容量，在一定程度上克服了光照、姿态的不利因素，提高了人脸识别率。

Face recognition based on symmetrical kernel principal component analysis

In order to improve the practicability of face recognition technology,a new face recognition method was proposed by adopting the facial mirror symmetry and Kernel Principle Component Analysis(KPCA).Firstly,the original images were decomposed by wavelet transform,and the low frequency components could be obtained.Then,the odd symmetry samples and the even symmetry samples were obtained by mirror transforming.Odd/even eigen vector were separately extracted through KPCA and fused to composite features by an odd-even weighted factor.A nearest neighbor classifier was used to classify the images.The proposed method was tested on the ORL face image database.The experimental results show the method can increase the sample capacity,overcome the effect of illumination and posture,and raise the recognition rate.Besides,in the comprehensive performance,it is better than contrast method.