中文摘要：

针对掌纹采集受外界因素和噪声的影响较大，传统方法掌纹识别率低、鲁棒性差等问题，提出一种基于特征加权与核主成分分析的掌纹识别算法．首先采用Curvelet变换对掌纹图像进行分解，得到不同尺度和角度的轮廓系数，并对Curvelet系数进行加权融合操作；然后通过核主成分分析对掌纹特征进行降维处理，实现特征提取；最后采用相关向量机实现掌纹匹配，并采用PolyU掌纹图像对算法的性能进行测试．结果表明，与其他掌纹识别算法相比，该算法取得的掌纹识别率更高，且掌纹匹配的时间最短，可以满足掌纹实时识别要求．

英文摘要：

Aiming at the problems that palmprint acquisition was influenced by external factors and noise, palmprint recognition rate of traditional method was low and robustness was poor, we proposed a new palmprint recognition algorithm based on feature weighted and kernel principal component analysis. Firstly, palmprint image was decomposed by Curvelet transform, contour coefficients of different scales and angles were obtained, and Curvelet coefficients were weighted by the fusion operation. Secondly, feature extraction was realized by using kernel principal component analysis to reduce dimension of palmprint feature. Finally, relevance vector machine was used to realize palmprint matching, and the performance of the algorithm was tested by using PolyU palmprint image. The test results show that, compared with other palmprint recognition algorithms, the proposed algorithm has higher palmprint recognition rate, and palmprint matching time is the shortest, which can meet the requirements of real-time palmprint recognition.