Distance measures for PCA-based face recognition
Distance measures for PCA-based face recognition
复制标题
DOI：
10.1016/j.patrec.2004.01.011
复制
发表时间：
2004-04-19
影响因子：
5.1
通讯作者：
Perlibakas, V
中科院分区：
文献类型：
--
作者：
Perlibakas, V
针对基于主成分分析(PCA)的人脸识别方法，比较了14种特征向量之间的距离度量及其改进方法的识别性能，提出了改进的基于和平方误差(SSE)的距离。使用包含423个人的照片的数据库进行识别实验。实验表明，对于不同的生物特征，提出的距离度量是前三个最好的度量之一。采用简化的马氏距离、加权的角度距离、改进的SSE距离、白化特征向量之间的角度距离，获得了最好的识别效果。使用改进的SSE距离，我们需要提取更少的图像才能达到100%的累积识别，而不是使用任何其他测试距离度量。我们还表明，与单独使用距离相比，使用距离度量的算法组合可以获得更好的识别结果。(C)2004爱思唯尔B.V.保留所有权利。
In this article we compare 14 distance measures and their modifications between feature vectors with respect to the recognition performance of the principal component analysis (PCA)-based face recognition method and propose modified sum square error (SSE)-based distance. Recognition experiments were performed using the database containing photographies of 423 persons. The experiments showed, that the proposed distance measure was among the first three best measures with respect to different characteristics of the biometric systems. The best recognition results were achieved using the following distance measures: simplified Mahalanobis, weighted angle-based distance, proposed modified SSE-based distance, angle-based distance between whitened feature vectors. Using modified SSE-based distance we need to extract less images in order to achieve 100% Cumulative recognition than using any other tested distance measure. We also showed that using the algorithmic combination of distance measures we can achieve better recognition results than using the distances separately. (C) 2004 Elsevier B.V. All rights reserved.