基于判别核窗宽的掌纹识别方法

提出了一种新的判别 核窗宽方法,进而研究了基于判别核窗宽的KPCA和LPP在掌纹识别中的应用。首先根据训练 样本和类标签计 算类内核窗宽和类间核窗宽；在分类密集区选择较小窗宽,在分类稀疏区选择较大窗宽,可 以有效提取数 据的关联特征；然后运用基于判别核窗宽的KPCA和LPP方法提取低维特征向量,计算特征向 量间的余弦 距离进行掌纹匹配；最后运用PolyU掌纹图像库,对本文算法进行测试。实验结果表明,与 传统算法相比, 本文算法的识别率最高,识别时间小于0.6s,验证了方法的有效性 。

A new palmprint recognition method based on discriminant kernel width

A new discriminant kernel width method is proposed.Based on this method,the application of KPCA and LPP based on discriminant kernel width method for palmpr int recognition is studied. Firstly,the within-class kernel width and the between-class kernel width are calculated by the training dataset and its label.The smaller kernel width is selected for the clusters of dense distri butions.On the contrary,the larger kernel width is selected for the clusters of sparse distributions.Secondly,KPC A and LPP based on discriminant kernel width method are used to extract the low dimensionality feature vectors. The cosine distance between two feature vectors is calculated to match palmprints.Finally,the new algorithm is tested on PolyU palmprint database. The experimental results show that compared with the traditional method,the rec ognition rate (RR) of the new algorithm is higher.The time for palmprint recognition is smaller than 0.6s.The effectiveness of the proposed method is verified.