基于测地距离的KPCA人脸识别

针对人脸检测数据集中的信息均为高维特征向量且人脸识别易受表情变化影响等问题,本文提出一种基于测地距离的KPCA人脸识别方法,该方法利用非线性方法提取主成分。先采用KPCA方法把人脸数据映射到高维空间,进而在高维空间中提取人脸的主成分,其中核函数为多项式核函数;然后引入测地距离替换原来的欧氏距离进行相似度量,其能更准确地测量出两像素点间的实际距离,使得人脸识别率受表情变化影响小。该方法不但可以实现降维,而且还能达到有效提取特征的目的。在ORL人脸库上的实验结果表明,该方法的识别率明显优于PCA、KPCA等方法的识别率。

KPCA face recognition based on geodesic distance

Aiming at the problem that the information of the face detection data is high eigenvector and face recognition is easily affected by expression changes, we propose a kernel principle component analysis (KPCA) face recognition method based on geodesic distance. We extract principal components by the nonlinear method. First, we adopt the KPCA method to map face data to the high dimensional space, and then principal components of the face are extracted in the high dimensional space, where the kernel function is a polynomial kernel. Finally, geodesic distance is introduced to replace the original Euclidean distance as the similarity measure, which can more accurately measure the actual distance between two points, and face recognition rate is less affected by expression changes as well. The proposed method cannot only achieve dimension reduction, but also achieve effective data extraction. Experiments on the ORL face database show that the recognition rate of the proposed method is superior to that of the PCA and the KPCA.