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清华大学学报(自然科学版)  2021, Vol. 61 Issue (1): 77-88    DOI: 10.16511/j.cnki.qhdxxb.2020.21.016
  电子工程 本期目录 | 过刊浏览 | 高级检索 |
三维人脸识别研究进展综述
罗常伟1,於俊2,于灵云2,李亚利1,王生进1,*()
1. 清华大学 电子工程系, 北京 100084
2. 中国科学技术大学 自动化系, 合肥 230027
Overview of research progress on 3-D face recognition
Changwei LUO1,Jun YU2,Lingyun YU2,Yali LI1,Shengjin WANG1,*()
1. Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2. Department of Automation, University of Science and Technology of China, Hefei 230027, China
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摘要 

近年来,三维人脸识别研究取得了较大进展。相比二维人脸识别,三维人脸识别更具有优势,主要特点是在识别中利用了三维形状数据。该文首先根据三维形状数据的来源,将三维人脸识别分为基于彩色图像的三维人脸识别、基于高质量三维扫描数据的三维人脸识别、基于低质量RGB-D图像的三维人脸识别,分别阐述了各自具有代表性的方法及其优缺点;其次分析了深度学习在三维人脸识别中的应用方式;然后分析了三维人脸数据与二维图像在双模态人脸识别中的融合方法,并介绍了常用的三维人脸数据库;最后讨论了三维人脸识别面临的主要困难及发展趋势。

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罗常伟
於俊
于灵云
李亚利
王生进
关键词 三维人脸识别三维数据深度图像    
Abstract

Research on 3-D face recognition has made great progress in recent years. 3-D face recognition is more effective than 2-D face recognition. Its main feature is the use of 3-D shape data for recognition. The 3-D face recognition methods are categorized into three types based on the source of the 3-D shape data with methods based on 2-D color images, high quality 3-D scanning data, and low quality RGB-D images. This study reviews these methods and discusses their advantages and disadvantages. This paper then reviews the use of deep learning methods for 3-D face recognition. Besides, 3-D and 2-D face data fusion methods are reviewed for bi-modal face recognition. The commonly used 3-D face databases are also summarized. Finally, the main difficulties and future development trends of 3-D face recognition are discussed.

Key words3-D face recognition    3-D data    depth images
收稿日期: 2020-07-07      出版日期: 2020-11-26
通讯作者: 王生进     E-mail: wgsgj@tsinghua.edu.cn
引用本文:   
罗常伟,於俊,于灵云,李亚利,王生进. 三维人脸识别研究进展综述[J]. 清华大学学报(自然科学版), 2021, 61(1): 77-88.
Changwei LUO,Jun YU,Lingyun YU,Yali LI,Shengjin WANG. Overview of research progress on 3-D face recognition. Journal of Tsinghua University(Science and Technology), 2021, 61(1): 77-88.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2020.21.016  或          http://jst.tsinghuajournals.com/CN/Y2021/V61/I1/77
  基于彩色图像的二维和三维相结合的人脸识别方法[8]
  三维人脸曲面及其径向曲线表示[34]
  三维网格模型中查找中心面片的周围面片环的过程[52]
  三维人脸数据的对比[63]
10.16511/j.cnki.qhdxxb.2020.21.016.T001

常用的三维人脸数据库

数据库 人数 样本数 采集设备 彩色图 表情和姿态变化情况 数据类型
FRGC v2[84](2005年) 466 4 007 Minolta vivid 910 表情变化 depth image
CASIA-3D[85] (2006年) 123 4 624 Minolta vivid 910 表情变化 mesh
BU-3DFE[86](2006年) 100 2 500 3DMD digitizer 表情变化 mesh
Bosphorus[87](2008年) 105 4 666 Mega Capturor 表情和姿态变化 point cloud
UMB-DB[88](2011年) 143 1 473 Minolta vivid 910 表情变化 depth image
EURECOM[63](2013年) 52 936 Kinect 表情和姿态变化 depth image
IIIT-D[62] (2014年) 106 4 605 Kinect 表情和姿态变化 depth image
Lock3DFace[89] (2015年) 509 5 711 Kinect v2 表情和姿态变化 depth image
  
常用的三维人脸数据库
10.16511/j.cnki.qhdxxb.2020.21.016.T002

部分算法在FRGC v2和Bosphorus数据库上的首位识别率和数据模态

算法 首位识别率/% 模态
FRGC v2 Bosphorus
Mian等[47](2008年) 96.1 bi-modal
Queirolo等[24](2010年) 98.4 depth
Drira等[34] (2013年) 97.0 87.0 depth
Elaiwat等[46](2015年) 97.1 depth
Li等[65](2016年) 95.2 99.4 bi-modal
Lei等[48](2016年) 96.3 98.9 depth
Emambakhsh等[41](2017年) 97.9 95.4 depth
Kim等[56](2017年) 99.2 depth
Gilani等[59](2018年) 97.1 96.2 depth
Soltanpour[53](2019年) 99.3 94.8 depth
Jiang等[68] (2019年) 98.5 99.5 bi-modal
Cai等[60](2019年) 100 99.7 depth
  
部分算法在FRGC v2和Bosphorus数据库上的首位识别率和数据模态
1 WANG M, DENG W. Deep face recognition: A survey[Z/OL]. (2018-04-18)[2020-07-01]. https://arxiv.org/abs/1804.06655.
3 PATIL H , KOTHARI A , BHURCHANDI K . 3-D face recognition:Features, databases, algorithms and challenges[J]. Artificial Intelligence Review, 2015. 44 (3): 393- 441.
doi: 10.1007/s10462-015-9431-0
4 SOLTANPOUR S , BOUFAMA B , WU Q M . A survey of local feature methods for 3D face recognition[J]. Pattern Recognition, 2017. 72, 391- 406.
doi: 10.1016/j.patcog.2017.08.003
5 BLANZ V , VETTER T . Face recognition based on fitting a 3D morphable model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003. 25 (9): 1063- 1074.
doi: 10.1109/TPAMI.2003.1227983
7 PAYSAN P, KNOTHE R, AMBERG B, et al. A 3D face model for pose and illumination invariant face recognition[C]//20096th IEEE International Conference on Advanced Video and Signal Based Surveillance. Genova, Italy: IEEE, 2009: 296-301.
8 LIU F , ZHAO Q J , LIU X M , et al. Joint face alignment and 3D face reconstruction with application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. 42 (3): 664- 678.
doi: 10.1109/TPAMI.2018.2885995
9 JIANG D L , HU Y X , YAN S C , et al. Efficient 3D reconstruction for face recognition[J]. Pattern Recognition, 2005. 38 (6): 787- 798.
doi: 10.1016/j.patcog.2004.11.004
10 TANG H L , YIN B C , SUN Y F , et al. Pose-invariant face recognition based on a single view[J]. Journal of Information and Computational Science, 2010. 7 (12): 2369- 2376.
11 PRABHU U , HEO J , SAVVIDES M . Unconstrained pose-invariant face recognition using 3D generic elastic models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011. 33 (10): 1952- 1961.
doi: 10.1109/TPAMI.2011.123
12 ZHU X Y, LEI Z, YAN J J, et al. High-fidelity pose and expression normalization for face recognition in the wild[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 787-796.
13 HASSNER T, HAREL S, PAZ E, et al. Effective face frontalization in unconstrained images[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 4295-4304.
14 YIM J, JUNG H, YOO B, et al. Rotating your face using multi-task deep neural network[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE, 2015: 676-684.
15 KAN M N, SHAN S G, CHANG H, et al. Stacked progressive auto-encoders (SPAE) for face recognition across poses[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014: 1883-1890.
16 TANG C H, HSU G J, YAP M H. Face recognition with disentangled facial representation learning and data augmentation[C]//2019 IEEE International Conference on Image Processing. Taipei, China: IEEE, 2019: 1670-1674.
18 PAPATHEODOROU T, RUECKERT D. Evaluation of automatic 4D face recognition using surface and texture registration[C]//6th IEEE International Conference on Automatic Face and Gesture Recognition. Seoul, South Korea: IEEE, 2004: 321-326.
19 CHANG K J , BOWYER K W , FLYNN P J . Effects on facial expression in 3D face recognition[J]. Proceedings of Spie the International Society for Optical Engineering, 2005. 5779, 132- 143.
20 FALTEMIER T C , BOWYER K W , FLYNN P J . A region ensemble for 3-D face recognition[J]. IEEE Transactions on Information Forensics & Security, 2008. 3 (1): 62- 73.
21 MOHAMMADZADE H , HATZINAKOS D . Iterative closest normal point for 3D face recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013. 35 (2): 381- 397.
22 VIVEK E P , SUDHA N . Robust Hausdorff distance measure for face recognition[J]. Pattern Recognition, 2007. 40 (2): 431- 442.
doi: 10.1016/j.patcog.2006.04.019
23 YU Y , DA F P , GUO Y F . Sparse ICP with resampling and denoising for 3D face verification[J]. IEEE Transactions on Information Forensics and Security, 2019. 14 (7): 1917- 1927.
doi: 10.1109/TIFS.2018.2889255
24 QUEIROLO C C , SILVA L , BELLON O R P , et al. 3D face recognition using simulated annealing and the surface interpenetration measure[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010. 32 (2): 206- 219.
25 AMBERG B, KNOTHE R, VETTER T. Expression invariant 3D face recognition with a morphable model[C]//20088th IEEE International Conference on Automatic Face & Gesture Recognition. Amsterdam, Netherlands: IEEE, 2008: 1-6.
26 HAAR T , VELTKAMP R C . Expression modeling for expression-invariant face recognition[J]. Computers & Graphics, 2010. 34 (3): 231- 241.
27 BOOTH J , ROUSSOS A , PONNIAH A , et al. Large scale 3D morphable models[J]. International Journal of Computer Vision, 2018. 2018 (126): 233- 254.
28 PAN G , WU Z H . 3D face recognition from range data[J]. International Journal of Image and Graphics, 2005. 5 (3): 573- 593.
doi: 10.1142/S0219467805001884
29 EFRATY B , BILGAZYEV E , SHAH S , et al. Profile-based 3D-aided face recognition[J]. Pattern Recognition, 2012. 45 (1): 43- 53.
30 LI Y , WANG Y , LIU J , et al. Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves[J]. Neurocomputing, 2018. 275, 1295- 1307.
doi: 10.1016/j.neucom.2017.09.070
31 SAMIR C , SRIVASTAVA A , DAOUDI M . Three-dimensional face recognition using shapes of facial curves[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2006. 28 (11): 1858- 1863.
32 SAMIR C , SRIVASTAVA A , DAOUDI M , et al. An intrinsic framework for analysis of facial surfaces[J]. International Journal of Computer Vision, 2009. 82 (1): 80- 95.
33 BERRETTI S , BIMBO A D , PALA P . 3D face recognition using isogeodesic stripes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2010. 32 (12): 2162- 2177.
34 DRIRA H , AMOR B B , SRIVASTAVA A , et al. 3D face recognition under expressions, occlusions, and pose variations[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013. 35 (9): 2270- 2283.
35 LEI Y J , BENNAMOUN M , HAYAT M , et al. An efficient 3D face recognition approach using local geometrical signatures[J]. Pattern Recognition, 2014. 47 (2): 509- 524.
doi: 10.1016/j.patcog.2013.07.018
36 CHANG K T , BOWYER K W , FLYNN P J . An evaluation of multimodal 2D+3D face biometrics[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005. 27 (4): 619- 624.
37 PASSALIS G , PERAKIS P , THEOHARIS T , et al. Using facial symmetry to handle pose variations in real-world 3D face recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2011. 33 (10): 1938- 1951.
38 LIU P J , WANG Y H , HUANG D , et al. Learning the spherical harmonic features for 3-D face recognition[J]. IEEE Transactions on Image Processing, 2013. 22 (3): 914- 925.
doi: 10.1109/TIP.2012.2222897
39 GUPTA S, AGGARWAL K, MARKEY K, et al. 3D face recognition founded on the structural diversity of human faces[C]//2007 IEEE Conference on Computer Vision & Pattern Recognition. Minneapolis, USA: IEEE, 2007: 1-7.
40 PERAKIS P , PASSALIS G , THEOHARIS T , et al. 3D facial landmark detection under large yaw and expression variations[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013. 35 (7): 1552- 1564.
41 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.
doi: 10.1109/TPAMI.2016.2565473
42 BERRETTI S, DEL A, PALA P. 3D partial face matching using local shape descriptors[C]//Joint ACM Workshop on Human Gesture and Behavior Understanding. New York, USA: ACM, 2011: 65-71.
43 INAN T , HALICI U . 3-D face recognition with local shape descriptors[J]. IEEE Transactions on Information Forensics & Security, 2012. 7 (2): 577- 587.
44 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.
doi: 10.1016/j.cviu.2012.10.002
45 LI H B , HUANG D , MORVAN J M , et al. Towards 3D face recognition in the real:A registration-free approach using fine-grained matching of 3D keypoint descriptors[J]. International Journal of Computer Vision, 2015. 113 (2): 128- 142.
doi: 10.1007/s11263-014-0785-6
46 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.
doi: 10.1016/j.patcog.2014.10.013
47 MIAN S , OWENS M B R . Keypoint detection and local feature matching for textured 3D face recognition[J]. International Journal of Computer Vision, 2008. 79 (1): 1- 12.
48 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.
doi: 10.1016/j.patcog.2015.09.035
50 HUANG D , ARDABILIAN M , WANG Y , et al. 3-D face recognition using eLBP-based facial description and local feature hybrid matching[J]. IEEE Transactions on Information Forensics & Security, 2012. 7 (5): 1551- 1565.
51 TANG H L , YIN B C , SUN Y F , et al. 3D face recognition using local binary patterns[J]. Signal Processing, 2013. 93 (8): 2190- 2198.
doi: 10.1016/j.sigpro.2012.04.002
52 WERGHI N , BERRETTI S , DEL BIMBOQ A . The mesh-LBP:A framework for extracting local binary patterns from discrete manifolds[J]. IEEE Transactions on Image Processing, 2015. 24 (1): 220- 235.
53 SOLTANPOUR S , WU Q M J . Weighted extreme sparse classifier and local derivative pattern for 3D face recognition[J]. IEEE Transactions on Image Processing, 2019. 28 (6): 3020- 3033.
doi: 10.1109/TIP.2019.2893524
54 WANG Y M , LIU J Z , TANG X O . Robust 3D face recognition by local shape difference boosting[J]. IEEE Transations on Pattern Analysis and Machine Intelligence, 2010. 32 (10): 1858- 1870.
doi: 10.1109/TPAMI.2009.200
55 HARIRI W , TABIA H , FARAH N , et al. 3D face recognition using covariance based descriptors[J]. Pattern Recognition Letters, 2016. 78, 1- 7.
doi: 10.1016/j.patrec.2016.03.028
56 KIM D, HERNANDEZ M, CHOI J, et al. Deep 3D face identification[C]//2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE, 2017: 133-142.
57 LI H B, SUN J, CHEN L M. Location-sensitive sparse representation of deep normal patterns for expression-robust 3D face recognition[C]//2017 IEEE International Joint Conference on Biometrics (IJCB). Denver, USA: IEEE, 2017: 234-242.
58 PARKHI O M, VEDALDI A, ZISSERMAN A. Deep face recognition[C]//Proceedings of the British Machine Vision Conference. Swansea, UK: BMVA Press, 2015: 41.1-41.12.
59 GILANI S Z, MIAN A. Learning from millions of 3D scans for large-scale 3D face recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake city, USA: IEEE, 2018: 1896-1905.
60 CAI Y , LEI Y J , YANG M L , et al. A fast and robust 3D face recognition approach based on deeply learned face representation[J]. Neurocomputing, 2019. 363 (21): 375- 397.
61 HSU G S , LIU Y L , PENG H C , et al. RGB-D-based face reconstruction and recognition[J]. IEEE Transactions on Information Forensics and Security, 2014. 9 (12): 2110- 2118.
doi: 10.1109/TIFS.2014.2361028
62 GOSWAMI G , VATSA M , SINGH R . RGB-D face recognition with texture and attribute features[J]. IEEE Transations on Information Forensics and Security, 2014. 9 (10): 1629- 1640.
doi: 10.1109/TIFS.2014.2343913
63 MIN R , KOSE N , DUGELAY J . KinectFaceDB:A Kinect database for face recognition[J]. IEEE Transactions on SMC:Systems, 2014. 44 (11): 1534- 1548.
64 XU X X , LI W , XU D . Distance metric learning using privileged information for face verification and person reidentification[J]. IEEE Transactions on NNLS, 2015. 26 (12): 3150- 3162.
65 LI B Y L , XUE M L , MIAN A S , et al. Robust RGB-D face recognition using kinect sensor[J]. Neurocomputing, 2016. 214, 93- 108.
doi: 10.1016/j.neucom.2016.06.012
66 LEE Y, CHEN J C, TSENG C, et al. Accurate and robust face recognition from RGB-D images with a deep learning approach[C]//Proceedings of the British Machine Vision Conference. York, UK.: IEEE, 2016: 1-14.
67 ZHANG H, HAN H, CUI J Y, et al. RGB-D face recognition via deep complementary and common feature learning[C]//IEEE International Conference on Automatic Face & Gesture Recognition. Xi'an, China: IEEE, 2018: 8-15.
68 JIANG L , ZHANG J Y , DENG B L . Robust RGB-D face recognition using attribute-aware loss[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.
doi: 10.1109/TPAMI.2019.2919284
69 LUO C W , ZHANG J Y , YU J , et al. Real-time head pose estimation and face modeling from a depth image[J]. IEEE Transactions on Multimedia, 2019. 21 (10): 2473- 2481.
doi: 10.1109/TMM.2019.2903724
70 MEYER G P, DO M N. Real-time 3D face verification with a consumer depth camera[C]//201815th Conference on Computer &Robot Vision. Toronto, Canada: IEEE, 2018: 71-79.
71 KIM D, CHOI J, LEKSUT J T, et al. Accurate 3D face modeling and recognition from RGB-D stream in the presence of large pose changes[C]//2016 IEEE International Conference on Image Processing. Phoenix, USA: IEEE, 2016: 3011-3015.
72 MU G D, HUANG D, HU G S, et al. Led3D: A lightweight and efficient deep approach to recognizing low-quality 3D faces[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 5766-5775.
73 LI H B , SUN J , XU Z B , et al. Multimodal 2D+3D facial expression recognition with deep fusion convolutional neural network[J]. IEEE Transactions on Multimedia, 2017. 19 (12): 2816- 2831.
doi: 10.1109/TMM.2017.2713408
74 SU H, MAJI S, KALOGERAKIS E, et al. Multi-view convolutional neural networks for 3D shape recognition[C]//2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 945-953.
75 SHI B G , BAI S , ZHOU Z C , et al. DeepPano:A deep panoramic representation for 3-D shape recognition[J]. IEEE Signal Processing Letters, 2015. 22 (12): 2339- 2343.
doi: 10.1109/LSP.2015.2480802
76 WU Z R, SONG S R, KHOSLA A, et al. 3D ShapeNets: A deep representation for volumetric shapes[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 1912-1920.
77 QI C R, SU H, NIESSNER M, et al. Volumetric and multi-view CNNs for object classification on 3D data[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 5648-5656.
78 HAN Z Z , LIU Z B , HAN J W , et al. Unsupervised 3D local feature learning by circle convolutional restricted boltzmann machine[J]. IEEE Transactions on Image Processing, 2016. 25 (11): 5331- 5344.
doi: 10.1109/TIP.2016.2605920
79 QI C, SU H, MO K C, et al. PointNet: Deep learning on point sets for 3D classification and segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 77-85.
80 BENABDELKADER C , GRIFFIN P A . Comparing and combining depth and texture cues for face recognition[J]. Image & Vision Computing, 2005. 23 (3): 339- 352.
81 KUSUMA G P , CHUA C S . PCA-based image recombination for multimodal 2D+3D face recognition[J]. Image and Vision Computing, 2011. 29 (5): 306- 316.
doi: 10.1016/j.imavis.2010.12.003
82 XU C H , LI S , TAN T N , et al. Automatic 3D face recognition from depth and intensity Gabor features[J]. Pattern Recognition, 2009. 42 (9): 1895- 1905.
doi: 10.1016/j.patcog.2009.01.001
83 CUI J Y, HAN H, SHAN S G, et al. RGB-D face recognition: A comparative study of representative fusion schemes[C]//Chinese Conference on Biometric Recognition. Urumqi, China: IEEE, 2018: 358-366.
84 PHILLIPS P J, FLYNN P J, SCRUGGS T, et al. Overview of the face recognition grand challenge[C]//2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005: 947-954.
85 XU C H, TAN T N, LI S, et al. Learning effective intrinsic features to boost 3D-based face recognition[C]//ECCV 2006, Lecture Notes in Computer Science. Graz, Austria: Springer-Verlag, 2006: 416-427.
86 YIN L J, WEI X Z, SUN Y, et al. A 3D facial expression database for facial behavior research[C]//7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE, 2006: 211-216.
87 SAVRAN A, NESE A, HAMDI D, et al. Bosphorus database for 3D face analysis[C]//Biometrics and Identity Management, First European Workshop. Roskilde, Denmark: IEEE, 2008: 47-56.
88 COLOMBO A, CUSANO C, SCHETTINI R. UMB-DB: A database of partially occluded 3D faces[C]//2011 IEEE International Conference on Computer Vision Workshops. Barcelona, Spain: IEEE, 2011: 2113-2119.
89 ZHANG J J, HUANG D, WANG Y H, et al. Lock3DFace: A large-scale database of low-cost Kinect 3D faces[C]//International Conference on Biometrics (ICB). Halmstad, Sweden: IEEE, 2016: 1-8.
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