基于最近邻插值和局部二值模式的人脸识别方法

Josky Aïzan, E. C. Ezin, C. Motamed
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引用次数: 3

摘要

在本文中,我们提出了一种新的人脸识别方法,该方法由人脸特征向量的降维组成。首先对输入的人脸图像进行图像缩放。然后应用局部二值模式(LBP)算子将人脸图像划分为不重叠的区域。从每个区域提取LBP直方图,并将其连接成一个代表人脸图像的直方图。采用最近邻分类器,以卡方函数作为不相似度度量进行识别。利用ORL (Olivetti研究实验室)数据库进行了仿真实验,结果表明该方法的识别率为97.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Face Recognition Approach Based on Nearest Neighbor Interpolation and Local Binary Pattern
In this paper, we present a novel approachfor face recognition which consists of a dimensionalityreduction of face feature vectors. The image scaling is firstlyconducted on an input face image. Then we applied the LocalBinary Pattern (LBP) operator by dividing the face imageinto non-overlapped regions. LBP histograms are extractedfrom each region and concatenated into a single one thatrepresents the face image. Nearest neighbor classifier isused to perform recognition with Chi square function asa dissimilarity measure. Simulation experiments are doneusing the ORL (Olivetti Research Laboratory) databaseshowing the efficiency of the proposed approach with 97.5%as recognition rate.
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