多尺度加权LBP的人脸识别  

Face Recognition Based on Multiple Weight Local Binary Pattern

作　　者：王成[1] 郭飞[2] 赖雄鸣[3] 郑黎晓[1] 

机构地区：[1]华侨大学计算机科学与技术学院,福建厦门361021 [2]西安交通大学电子与信息工程学院计算机系软件与理论研究所,西安710049 [3]华侨大学机电及自动化学院,福建厦门361021

出　　处：《光电工程》2014年第4期82-88,共7页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(51305142;51305143;61300138);华侨大学人才引进科研启动基金资助项目(12Y0347)

摘　　要：针对传统局部二值模型(LBP)算子容易受到周围噪声点的干扰的缺点,提出了首先对图像进行Gauss滤波预处理,去除图像中的干扰噪声。针对传统LBP算子无法提取出非局部特征信息,提出一种新的基于多尺度加权的改进LBP(MWLBP)算子。MWLBP算子以不同大小的方型邻域为研究对象,将不同大小区域的LBP直方图进行加权求和。MWLBP比传统LBP算子提取的特征范围更大,在提取了局部特征的同时,保留了一定的非局部特征。相对于Gabor和其它特征提取方法,MWLBP算子在保留了多尺度特征的同时,能控制计算量大小。在ORL和Yale人脸数据库上的实验表明,Gauss滤波预处理确实能去除图像中的干扰噪声,提高识别准确率;MWLBP算子比传统的LBP算子、Gabor和其它特征提取方法减少了计算量,加快了分类器训练和人脸识别的速度,提高了准确率。In order to overcome the noise sensitivity disadvantages of traditional Local Binary Pattern （LBP）, this paper presents using Gauss filters to preprocess face images to remove interference noise. In order to overcome the no non-local feature extraction disadvantages of traditional LBP, this paper presents a new Multiple Weight Local Binary Pattern （MWLBP） operator. MWLBP operator weights sum spatial region histogram value of different sizes square type neighborhood regional LBP. Compared with traditional LBP, this new operator extracts features in a much larger area and can preserve a certain non-local features while extracting local features at the same time. Compared with Gabor feature and other feature extraction methods, this new operator can control the amount of calculation while preserving multiple scale features. Numerical experimental results in ORL and Yale face datasets show that Gauss filter preprocessing can remove interference noise, and improve recognition accuracy rate. MWLBP has smaller computational complexity, less classifier training time, faster operational efficiency and higher recognition accuracy rate than traditional LBP, Gabor feature and other feature extraction methods

关 键 词：人脸识别 局部二值模式 Gauss滤波预处理 多尺度加权 空间区域直方图 GABOR 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]