CAJ | 학술논문

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。
침대전통국부이치모형(LBP)산자용역수도주위조성점적간우적결점，제출료수선대도상진행Gauss려파예처리，거제도상중적간우조성。침대전통LBP산자무법제취출비국부특정신식，제출일충신적기우다척도가권적개진LBP(MWLBP)산자。MWLBP산자이불동대소적방형린역위연구대상，장불동대소구역적LBP직방도진행가권구화。MWLBP비전통 LBP산자제취적특정범위경대，재제취료국부특정적동시，보류료일정적비국부특정。상대우 Gabor 화기타특정제취방법，MWLBP 산자재보류료다척도특정적동시，능공제계산량대소。재 ORL 화Yale 인검수거고상적실험표명，Gauss 려파예처리학실능거제도상중적간우조성，제고식별준학솔；MWLBP 산자비전통적LBP산자、Gabor화기타특정제취방법감소료계산량，가쾌료분류기훈련화인검식별적속도，제고료준학솔。
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.