模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2014年
10期
894-899
,共6页
人脸识别%特征子空间%二维线性鉴别分析(2DLDA)%均匀模式的局部二值模式(ULBP)
人臉識彆%特徵子空間%二維線性鑒彆分析(2DLDA)%均勻模式的跼部二值模式(ULBP)
인검식별%특정자공간%이유선성감별분석(2DLDA)%균균모식적국부이치모식(ULBP)
Face Recognition%Eigensubspace%Two-Dimensional Linear Discriminant Analysis (2DLDA)%Uniform Local Binary Pattern(ULBP)
将图像层次化分割并提取各个图像子块的均匀模式的局部二值模式( ULBP)直方图特征，在考虑到全局及局部特征的同时，将处理空间从灰度空间投影到ULBP特征子空间，有效消除行向量之间的相关性，从而使应用行二维线性鉴别分析处理得到的鉴别投影矩阵性能更优。在ORL、YALE及FERET人脸库上与基于二维线性鉴别分析的方法及基于多级局部二值模式的方法对比，结果显示文中方法维数更低，识别率更高，从而验证文中方法的有效性。
將圖像層次化分割併提取各箇圖像子塊的均勻模式的跼部二值模式( ULBP)直方圖特徵，在攷慮到全跼及跼部特徵的同時，將處理空間從灰度空間投影到ULBP特徵子空間，有效消除行嚮量之間的相關性，從而使應用行二維線性鑒彆分析處理得到的鑒彆投影矩陣性能更優。在ORL、YALE及FERET人臉庫上與基于二維線性鑒彆分析的方法及基于多級跼部二值模式的方法對比，結果顯示文中方法維數更低，識彆率更高，從而驗證文中方法的有效性。
장도상층차화분할병제취각개도상자괴적균균모식적국부이치모식( ULBP)직방도특정，재고필도전국급국부특정적동시，장처리공간종회도공간투영도ULBP특정자공간，유효소제행향량지간적상관성，종이사응용행이유선성감별분석처리득도적감별투영구진성능경우。재ORL、YALE급FERET인검고상여기우이유선성감별분석적방법급기우다급국부이치모식적방법대비，결과현시문중방법유수경저，식별솔경고，종이험증문중방법적유효성。
The image is segmented at different levels to extract the uniform local binary pattern ( ULBP ) histogram features of the sub-block images. The global and local features are taken into account, and meanwhile the processing space is converted from the gray space to ULBP feature subspace. Consequently, the correlation between row vectors can be eliminated effectively. Thus, the discriminant projection matrix is performed better through row two-dimensional linear discriminant analysis (R2DLDA). Experimental results on ORL, YALE and FERET databases show that compared with some common methods based on 2DLDA and multilevel LBP, the proposed method achieves a higher recognition rate with a low feature dimension, which proves its effectiveness.
