3D palmprint identification combining blocked ST and PCA
3D palmprint identification combining blocked ST and PCA
复制标题
结合分块ST和PCA的3D掌纹识别
DOI：
10.1016/j.patrec.2017.10.008
复制
发表时间：
2017-12-01
影响因子：
5.1
通讯作者：
Zhang, David
中科院分区：
文献类型：
--
作者：
Bai, Xuefei;Gao, Nan;Zhang, David
三维掌纹技术由于其独特的优点而被广泛研究，作为一种身份识别方法。为克服样本量小和一对多识别速度慢的局限性，提出了一种结合分块表面类型（ST）特征和主成分分析（PCA）的三维掌纹识别方法。该方法采用分块ST的直方图作为有效的掌纹特征，从而降低了后续的计算复杂度。使用直方图方法降低数据的维数，并使用PCA进一步压缩3D信息。最近邻分类器用作识别人的区分标准。两个数据库的实验结果表明了该方法的有效性。与其他单特征方法相比，该方法克服了传统方法的小样本问题，降低了计算复杂度，实现了准确、快速、鲁棒的识别。因此，该方法特别适用于大规模数据库。(C)2017爱思唯尔B.V.保留所有权利。
Three dimensional (3D) palmprint technologies have been widely studied as a method of human identification and recognition, because they offer unique merits over their 2D counterpart. To overcome the limitations of small sample sizes and low one-to-many identification speed, a novel method by combining the blocked surface type (ST) feature and principal component analysis (PCA) has been developed for 3D palmprint identification. This method adopts the histogram of blocked ST as an effective palmprint feature, thus reducing the subsequent computational complexity. The dimensionality of the data is reduced using the histogram method, and the 3D information is further compressed using PCA. A nearest neighbor classifier acts as the discrimination criterion for identifying a person. Experimental results using two databases demonstrate the effectiveness of the proposed method. Compared with other single-feature methods, the proposed approach overcomes the traditional problem of small sample sizes, reduces the computational complexity, and enables accurate, fast, and robust identification. Therefore, the proposed method is especially suitable for large-scale databases. (C) 2017 Elsevier B.V. All rights reserved.