中文摘要：

提出了基于核主元分析（KPCA）FLD相结合的掌纹识别方法。对每幅掌纹图像应用KPCA进行降维，然后将二维图像矩阵转换成一维图像矢量。PolyU掌纹图像库中所有图像矢量组成的数据矩阵作为FLD的输入，进行特征提取，计算特征矢量间的余弦距离进行掌纹匹配。实验结果说明，与传统的PCA＋FLD相比，在不同的特征个数下，本文方法均取得了较小的等错率（EER），而且特征提取时间较短，运行速度较快。在三种不同的核函数中，RBF核函数的识别效果最佳，等错率最小为0。

英文摘要：

A novel method for palmprint recognition based on kernel principal component analysis（KPCA） and fisher linear discriminant（FLD） is presented. After the utilization of KPCA as a pre-processing step to reduce the dimensionality of a palmprint image,the 2D image matrices are then transformed into 1D image vectors. FLD has been used to extract feature vectors for all palmprint image vectors of PolyU palmprint database. Then the cosine distances between feature vectors are calculated to match palmprints. The experiment results show that the new method has lower equal error rate（EER） ,shorter time for the feature extraction and faster running speed than the traditional method when the principal component numbers are different. The recognition performance of radial basis function is the best among the three different types of kernels,because the equal error rate are zero.