改进的基于二维主分量分析的掌纹识别

主分量分析(PCA)是一种在众多生物特征识别中获得成功应用的特征提取技术,是一种基于二阶统计的在最小均方误差意义上的最优维数据压缩技术,它所提取的各特征分量之间是互不相关的。传统的PCA变换是对图像向量的分析,但向量维数一般都很高。二维主分量分析方法是最近兴起的针对图像矩阵的主分量分析方法,与一维主分量分析相比能更精确的计算原始数据的协方差矩阵。将其应用于掌纹识别,并在主分量的选取上加以改进,选取了更适合于分类的主分量。实验结果表明,该方法不仅有更高的识别率,而且维数更低。

Palm-print recognition based on improved two dimensions principal component analysis

Principal component analysis(PCA) is a successful feature detection method for pattern recognition.It is the optimal dimension compression technique based on second-order information in the sense of mean-square error.It deals with image vector whose dimension is usually high.Two dimensions PCA is a novel PCA method for image matrix,and it can calculate the covariance matrix more precise.The new two dimensions PCA method is applied to palm-print recognition.An improvement is made in the selection of principal components.In method selected the principal component it is a better classification.The method is applied to PolyU Palm-print Database.The experiment result shows that this method can get more recognition rate with lower dimensions.