[发明专利]一种人脸图像凸凹模式纹理特征提取及识别方法

说明书

技术领域

本发明涉及一种人脸图像凸凹模式纹理特征提取及识别方法，属于模式识别技术领域。

背景技术

局部二值模式(Local binary pattern,LBP)[L.Wang and D.C.He,“Texture classification using texture spectrum”,Pattern Recognition,vol.23,pp.905-910,1990.]是一种重要的图像特征提取算子，具有计算量小和有效的特点。尽管LBP在计算机视觉和模式识别领域已经获得了很大的成功，但其工作机制仍有值得改进之处。显性局部二值模式(Dominant local binary patterns,DLBP)[S.Liao,M.W.K.Law,and A.C.S.Chung,“Dominant local binary patterns for texture classification,”IEEE Trans.Image Process.,vol.18,no.5,pp.1107–1118,May 2009.]在统计图像的LBP所有模式基础上，筛选出较高频率的模式，并把累积频率达到80％的高频率模式组成最终的特征向量。LBP只考虑到中心像素与周围像素差值的符号信息，完备局部二值模式(Completed local binary pattern,CLBP)[Z.Guo,L.Zhang and D.Zhang,“A completed modeling of local binary pattern operator for texture classification,”IEEE Trans.Image Process.,vol.19,no.6,pp.1657-1663,2010.]不仅考虑了符号信息，还考虑了差值的幅度信息及中心像素点的特征。LBP提取的是图像的一阶微分信息，局部微分模式(Local derivative pattern,LDP)[B.Zhang,Y.Gao,S.Zhao,and J.Liu,“Local derivative pattern versus local binary pattern:Face recognition with higher-order local pattern descriptor,”IEEE Trans.Image Process.,vol.19,no.2,pp.533–544,Feb.2010.]改进了LBP算法，提取了图像的二阶微分信息。为了减少LBP算法中模式的数目，研究人员提出了中心对称局部微分模式(Center-Symmetric Local derivative Pattern,CS-LDP)[G.Xue,L.Song,J.Sun,M.Wu，Hybrid Center-Symmetric Local Pattern for Dynamic Background Subtraction,ICME,Barcelona,Spain(2011),pp.1–6,July 2011.]和中心对称局部二值模式算法(Center-symmetric local binary pattern,CS-LBP)[Marko H,Matti P,Cordelia S.Description of interest regions with center-symmetric local binary pattern[C]//Conference on Computer Vision Graphics and Image Processing.2006,4338:58-69]。局部二值计数(Local binary count,LBC)[Zhao Y,Huang D S,Jia W,“Completed local binary count for rotation invariant texture classification,”IEEE Trans.Image Process.,vol.21,no.10,pp.4492-4497,2012.]只考虑二值模式中模式为“1”的个数。统一局部二值模式减少了模式数目，减少了计算量[T.Ojala,M.T.“Gray scale and rotation invariant texture classification with local binary patterns,”in:D.Vernon(Ed.),Proceedings of the Sixth European Conference on Computer Vision(ECCV2000),Dublin,Ireland,pp.404–420,2000.]。为了增强LBP算法所提取纹理的鉴别性，LBP算法也与Gabor滤波器和一些数据降维算法结合起来[Zhang W C，Shan S G，Gao W，et a1.Local Gabor Binary Pattern Histogram Sequence.(LGBPHS)：A Novel Non-Statistical Model for Face Representation and Recognition[C]Proc of the 10th IEEE Int’l Conf on Computer Vision，2005：786—791.；B.Zhang,S.Shan,X.Chen,and W.Gao,“Histogram of Gabor Phase Patterns(HGPP):A novel object representation approach for face recognition,”IEEE Trans.Image Process.,vol.16,no.1,pp.57–68,2007.]。