基于局部特征的表情不变3维人脸识别算法
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摘要
为了减少表情变化带来的影响,提出一种基于人脸几何特征和局部描述子的3维人脸识别算法.首先利用多尺度形状变化指数在3维人脸上检测出关键点.然后提出一种基于关键点的2步匹配算法,以提高识别算法的效率:第1步在关键点上提取3维法向量分布直方图描述子,将测试集人脸与库集人脸上的描述子进行匹配,除去匹配程度较低的一部分库集人脸,减少后续匹配的人脸数;第2步在关键点上提取协方差矩阵描述子,再将测试集人脸与剩余的库集人脸在给定的约束条件下进行协方差矩阵描述子匹配.最后用成功匹配的关键点个数衡量人脸的匹配程度,得到分类结果.在Bosphorus, FRGC v2.0和BU-3DFE数据库上进行实验的结果表明,文中算法取得了良好的识别效果,对3维人脸的表情变化有较好的鲁棒性,同时在识别速度上也优于已有的许多算法.
A novel 3D face recognition algorithm using geometry and local shape descriptors was proposed to overcome the influence of expression variations. At first, multiscale shape variation indexes were calculated to locate keypoints on the 3D face. Then, a two-step matching method was proposed to improve the efficiency: a large number of irrelevant candidate faces were eliminated based on the extracted 3D histograms of normal distributions at first and then the keypoints of the remaining faces were matched based on the covariance matrix descriptor generated as local shape descriptors. Finally, the similarity of two faces was measured by the number of the keypoints that can be correctly matched. The experiments of the proposed algorithm were carried out on the Bosphorus, FRGC v2.0 and BU-3DFE datasets and achieved superior recognition performance. The results demonstrate that the proposed algorithm is robust to expression variations and outperforms the state-of-the-art algorithms in term of the recognition speed.
A novel 3D face recognition algorithm using geometry and local shape descriptors was proposed to overcome the influence of expression variations. At first, multiscale shape variation indexes were calculated to locate keypoints on the 3D face. Then, a two-step matching method was proposed to improve the efficiency: a large number of irrelevant candidate faces were eliminated based on the extracted 3D histograms of normal distributions at first and then the keypoints of the remaining faces were matched based on the covariance matrix descriptor generated as local shape descriptors. Finally, the similarity of two faces was measured by the number of the keypoints that can be correctly matched. The experiments of the proposed algorithm were carried out on the Bosphorus, FRGC v2.0 and BU-3DFE datasets and achieved superior recognition performance. The results demonstrate that the proposed algorithm is robust to expression variations and outperforms the state-of-the-art algorithms in term of the recognition speed.
引文
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[12]Tang Y H,Li H B,Sun X,et al.Principal curvature measures estimation and application to 3D face recognition[J].Journal of Mathematical Imaging and Vision,2017,59(2):211-233
[13]Deng X,Da F P,Shao H J.Efficient 3D face recognition using local covariance descriptor and Riemannian kernel sparse coding[J].Computers&Electrical Engineering,2017,62:81-91
[14]Ma B P,Su Y,Jurie F.Covariance descriptor based on bio-inspired features for person re-identification and face verification[J].Image and Vision Computing,2014,32(6/7):379-390
[15]Sra S.A new metric on the manifold of kernel matrices with application to matrix geometric means[C]//Proceedings of the25th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2012:144-152
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[17]Yin L J,Wei X Z,Sun Y,et al.A 3D facial expression database for facial behavior research[C]//Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition.Los Alamitos:IEEE Computer Society Press,2006:211-216
[18]Phillips P J,Flynn P J,Scruggs T,et al.Overview of the face recognition grand challenge[C]//Proceedings of the IEEEComputer Society Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2005,1:947-954
[19]Berretti S,Werghi N,del Bimbo A,et al.Selecting stable keypoints and local descriptors for person identification using3D face scans[J].The Visual Computer,2014,30(11):1275-1292
[20]Zaharescu A,Boyer E,Varanasi K,et al.Surface feature detection and description with applications to mesh matching[C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2009:373-380
[21]Guo Y L,Lei Y J,Liu L,et al.EI3D:expression-invariant 3Dface recognition based on feature and shape matching[J].Pattern Recognition Letters,2016,83(3):403-412
[22]Kim D,Hernandez M,Choi J,et al.Deep 3D face identification[C]//Proceedings of the IEEE International Joint Conference on Biometrics.Los Alamitos:IEEE Computer Society Press,2017:133-142
[23]Li Y,Wang Y H,Liu J,et al.Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves[J].Neurocomputing,2018,275:1295-1307
[2]Berretti S,Werghi N,del Bimbo A,et al.Selecting stable keypoints and local descriptors for person identification using 3Dface scans[J].The Visual Computer,2014,30(11):1275-1292
[3]Abbad A,Abbad K,Tairi H.3D face recognition:multi-scale strategy based on geometric and local descriptors[J].Computers&Electrical Engineering,2018,70:525-537
[4]Lei Y J,Bennamoun M,El-Sallam A A.An efficient 3D face recognition approach based on the fusion of novel local low-level features[J].Pattern Recognition,2013,46(1):24-37
[5]Ming Y.Rigid-area orthogonal spectral regression for efficient3D face recognition[J].Neurocomputing,2014,129:445-457
[6]Quan W,Matuszewski B J,Shark L K.Statistical shape modelling for expression-invariant face analysis and recognition[J].Pattern Analysis and Applications,2016,19(3):765-781
[7]Cheng S Y,Marras I,Zafeiriou S,et al.Statistical non-rigid ICP algorithm and its application to 3D face alignment[J].Image and Vision Computing,2017,58:3-12
[8]Chang Pengpeng,Da Feipeng,Mei Jun.Expression-robustness3D face recognition based on facial profiles[J].Journal of Image and Graphics,2015,20(3):332-339(in Chinese)(常朋朋,达飞鹏,梅俊.对表情鲁棒的面部轮廓线3维人脸识别[J].中国图象图形学报,2015,20(3):332-339)
[9]Emambakhsh M,Evans A.Nasal patches and curves for expression-robust 3D face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(5):995-1007
[10]Smeets D,Keustermans J,Vandermeulen D,et al.meshSIFT:local surface features for 3D face recognition under expression variations and partial data[J].Computer Vision and Image Understanding,2013,117(2):158-169
[11]Lei Y J,Guo Y L,Hayat M,et al.A two-phase weighted collaborative representation for 3D partial face recognition with single sample[J].Pattern Recognition,2016,52:218-237
[12]Tang Y H,Li H B,Sun X,et al.Principal curvature measures estimation and application to 3D face recognition[J].Journal of Mathematical Imaging and Vision,2017,59(2):211-233
[13]Deng X,Da F P,Shao H J.Efficient 3D face recognition using local covariance descriptor and Riemannian kernel sparse coding[J].Computers&Electrical Engineering,2017,62:81-91
[14]Ma B P,Su Y,Jurie F.Covariance descriptor based on bio-inspired features for person re-identification and face verification[J].Image and Vision Computing,2014,32(6/7):379-390
[15]Sra S.A new metric on the manifold of kernel matrices with application to matrix geometric means[C]//Proceedings of the25th International Conference on Neural Information Processing Systems.New York:Curran Associates Inc,2012:144-152
[16]Savran A,Alyüz N,Dibeklio?lu H,et al.Bosphorus database for 3D face analysis[M]//Biometrics and Identity Management.Heidelberg:Springer,2008:47-56
[17]Yin L J,Wei X Z,Sun Y,et al.A 3D facial expression database for facial behavior research[C]//Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition.Los Alamitos:IEEE Computer Society Press,2006:211-216
[18]Phillips P J,Flynn P J,Scruggs T,et al.Overview of the face recognition grand challenge[C]//Proceedings of the IEEEComputer Society Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEE Computer Society Press,2005,1:947-954
[19]Berretti S,Werghi N,del Bimbo A,et al.Selecting stable keypoints and local descriptors for person identification using3D face scans[J].The Visual Computer,2014,30(11):1275-1292
[20]Zaharescu A,Boyer E,Varanasi K,et al.Surface feature detection and description with applications to mesh matching[C]//Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition.Los Alamitos:IEEEComputer Society Press,2009:373-380
[21]Guo Y L,Lei Y J,Liu L,et al.EI3D:expression-invariant 3Dface recognition based on feature and shape matching[J].Pattern Recognition Letters,2016,83(3):403-412
[22]Kim D,Hernandez M,Choi J,et al.Deep 3D face identification[C]//Proceedings of the IEEE International Joint Conference on Biometrics.Los Alamitos:IEEE Computer Society Press,2017:133-142
[23]Li Y,Wang Y H,Liu J,et al.Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves[J].Neurocomputing,2018,275:1295-1307