基于奇异点邻域结构的三维人脸识别方法
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摘要
提出一种新的基于奇异点邻域结构的三维人脸识别方法.首先,在人脸纹理图像上分割目标区域,划分特征子区域,提取二维奇异点和奇异点邻域结构;然后,在人脸空间几何信息上标记三维奇异点及其邻域结构,并以奇异点和奇异点邻域结构的三维信息表征人脸特征;最后,采用奇异点邻域结构最近点方法识别人脸身份信息.实验结果表明,所提出方法在三维人脸识别方面具有较高的识别准确率和较好的鲁棒性能.
A novel 3D face recognition approach based on singular point neighborhood structure is proposed in this paper. Firstly, by detecting feature sub-region in face texture image, two dimensional singular points and singular point neighborhood structure are extracted. Then three dimensional singular points and its neighborhood structure as facial features are marked in face vertex image. Finally, recognition results are obtained by using the method of nearest point of singular point neighborhood structure. Experiment results show that the proposed approach achieves good recognition results with higher recognition accuracy and good robustness.
A novel 3D face recognition approach based on singular point neighborhood structure is proposed in this paper. Firstly, by detecting feature sub-region in face texture image, two dimensional singular points and singular point neighborhood structure are extracted. Then three dimensional singular points and its neighborhood structure as facial features are marked in face vertex image. Finally, recognition results are obtained by using the method of nearest point of singular point neighborhood structure. Experiment results show that the proposed approach achieves good recognition results with higher recognition accuracy and good robustness.
引文
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[22]李燕春,达飞鹏.基于特征点表情变化的3维人脸识别[J].中国图象图形学报,2014,19(10):1459-1467.(Li Y C,Da F P.Expression-insensitive 3D face recognition method based on facial fiducial points[J].J of Image and Graphics,2014,19(10):1459-1467.)
[2]Zhuang L S,Chan T H,Yang A Y,et al.Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment[J].Int J of Computer Vision,2015,114(2/3):272-287.
[3]甘俊英,何国辉,何思斌.核零空间线性鉴别分析及其在人脸识别中的应用[J].计算机学报,2014,37(11):2374-2379.(Gan J Y,He G H,He S B.Kernel null space linear discriminant analysis and its applications in face recognition[J].Chinese J of Computers,2014,37(11):2374-2379.)
[4]龚劬,唐萍峰.基于大间距准则的不相关保局投影分析[J].自动化学报,2013,39(9):1575-1580.(Gong Q,Tang P F.Uncorrelated locality preserving projections analysis based on maximum margin criterion[J].Acta Automatica Sinica,2013,39(9):1575-1580.)
[5]马小虎,谭延琪.基于鉴别稀疏保持嵌入的人脸识别算法[J].自动化学报,2014,40(1):73-82.(Ma X H,Tan Y Q.Face recognition based on discriminant sparsity preserving embedding[J].Acta Automatica Sinica,2014,40(1):73-82.)
[6]阮越,陈汉武,刘志昊,等.量子主成分分析算法[J].计算机学报,2014,37(3):666-676.(Ruan Y,Chen H W,Liu Z H,et al.Quantum principal component analysis algorithm[J].Chinese J of Computers,2014,37(3):666-676.)
[7]Mohammadzade H,Hatzinakos D.Iterative closest normal point for 3D face recognition[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2013,35(2):381-397.
[8]Ganguly S,Bhattacharjee D,Nasipuri M.3D face recognition from range images based on curvature analysis[J].J on Image and Video Processing,2014,4(3):748-753.
[9]李晓莉,达飞鹏.基于排除算法的快速三维人脸识别方法[J].自动化学报,2010,36(1):153-158.(Li X L,Da F P.A rapid method for 3D face recognition based on rejection algorithm[J].Acta Automatica Sinica,2010,36(1):153-158.)
[10]李晓莉,达飞鹏.基于侧面轮廓线和刚性区域的3维人脸识别[J].中国图象图形学报,2010,15(2):266-273.(Li X L,Da F P.3D face recognition based on profile and rigid regions[J].J of Image and Graphics,2010,15(2):266-273.)
[11]Wang X,Ruan Q,Jin Y,et al.Three-dimensional face recognition under expression variation[J].Eurasip J on Image and Video Processing,2014(1):1-11.
[12]Mian A,Bennamoun M,Owens R.2D and 3D multimodal hybrid face recognition[C].Proc of the 9th European Conf on Computer Vision.Graz:Springer,2006:344-355.
[13]Kamencay P,Hudec R,Benco M,et al.2D-3D face recognition method based on a modified CCA-PCA algorithm[J].Int J of Advanced Robotic Systems,2014,11(1):36-43.
[14]Elaiwat S,Bennamoun M,Boussaid F,et al.A curvelet-based approach for textured 3D face recognition[J].Pattern Recognition,2015,48(4):1235-1246.
[15]Mian A,Bennamoun M,Owens R.Keypoint detection and local feature matching for textured 3D face recognition[J].Int J of Computer Vision,2008,79(1):1-12.
[16]Martinez B,Valstar M F,Binefa X,et al.Local evidence aggregation for regression based facial point detection[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2013,35(5):1149-1163.
[17]王跃明,潘纲,吴朝晖.三维人脸识别综述[J].计算机辅助设计与图形学学报,2008,20(7):819-829.(Wang Y M,Pan G,Wu Z H.A survey of 3D face recognition[J].J of Computer-Aided Design&Computer Graphics,2008,20(7):819-829.)
[18]Chen Y,Medioni G.Object modeling by registration of multiple range images[C].Proc of the Int Conf on Robotics and Automation.Sacramento:IEEE,1991:2724-2729.
[19]Besl P J,Mc Kay H D.A method for registration of 3-D shapes[J].IEEE Trans on Pattern Analysis and Machine Intelligence,1992,14(2):239-256.
[20]Phillips P J,Flynn P J,Scruggs T,et al.Overview of the face recognition grant challenge[C].Proc of the Computer Society Conf on Computer Vision and Pattern Recognition.San Diego:IEEE,2005:947-954.
[21]尹宝才,孙艳丰,王成章,等.BJUT-3D三维人脸数据库及其处理技术[J].计算机研究与发展,2009,46(6):1009-1018.(Yin B C,Sun Y F,Wang C Z,et al.BJUT-3D large scale 3D face database and information processing[J].J of Computer Research and Development,2009,46(6):1009-1018.)
[22]李燕春,达飞鹏.基于特征点表情变化的3维人脸识别[J].中国图象图形学报,2014,19(10):1459-1467.(Li Y C,Da F P.Expression-insensitive 3D face recognition method based on facial fiducial points[J].J of Image and Graphics,2014,19(10):1459-1467.)