基于二维主分量分析的人耳身份识别研究

有效的特征提取方法是解决人耳身份识别任务的关键之一。以主分量分析（PCA）为代表的线性子空间方法在特征提取工作中得到了广泛应用。为了更有效地提取人耳图像特征并减少运算量，将基于二维图像矩阵的2D-PCA方法应用于人耳身份识别。针对三个USTB人耳图像库，采用最近邻分类器，研究了选用不同的特征维数、贡献率，及不同的相似性测度时，2D-PCA方法与传统的PCA方法的识别性能。交叉验证的实验结果表明：2D-PCA方法较PCA方法获得了更短的训练时间和更高的识别率，说明基于图像矩阵的2D—PCA方法是一种效率更高，鲁棒性更强的人耳身份识别方法。

Ear Recognition Based on 2D Principal Component Analysis

Feature extraction is one of the essential techniques to solve the problems of ear recognition effectively.The principal component analysis(PCA) method,as a typical linear subspace method,is applied extensively to feature extraction.The intensity of calculation can be reduced significantly and features can be extracted more effectively if 2D-PCA method based on 2D image matrix is utilized for ear recognition.With the data from USTB human ear database 1,2,and 3,the recognition performance of 2D-PCA and PCA a...