Research Progress in Three-Dimensional Face Recognition

doi:10.3778/j.issn.1002-8331.2303-0401

Abstract

Abstract: Three-dimensional face recognition technology has unique advantages such as natural, convenient and user-friendly, and therefore has become a trending research direction in both academia and industry. In recent years, researchers have proposed various new methods to challenge 3D face recognition for different types of 3D face data such as monocular RGB color images, RGB-depth images, point cloud data, and multi-modal data, etc. According to the different formats of 3D face data, 3D face recognition methods are classified into 3D face reconstruction methods from monocular RGB images, face recognition methods based on point cloud data, RGB-depth image data, and multi-modal data. The principles, differences, and connections of various methods have been compared and analyzed. The latest progress of 3D face recognition research is comprehensively summarized, and the advantages and disadvantages of various technologies are expounded. Face databases in different formats are introduced, analyzing data collection methods, expression changes, posture changes. Finally, challenges faced by 3D face recognition technology and its future development direction are discussed.

Key words: 3D face, face recognition, feature extraction, 3D face database

摘要： 三维人脸识别技术具有自然、便捷、用户体验友好等独特优势，已成为学术界和工业界的一个热门研究方向。近几年，针对单目彩色图像、彩色图-深度图数据、点云数据、多模态数据等不同类型的三维人脸数据，研究者们提出了各种新方法来挑战三维人脸识别。根据三维人脸数据的不同格式，将三维人脸识别方法分为从单目彩色图像进行三维人脸重构的识别方法、基于点云数据的人脸识别方法、基于彩色图-深度图数据的人脸识别方法，以及基于多模态数据的人脸识别方法，对比分析了各类方法的原理、区别和联系，全面总结了三维人脸识别研究的最新进展，阐述了各类技术的优势和弊端。介绍了不同格式的人脸数据库，从数据收集方式、表情变化、姿态变化等方面进行分析，最后讨论了三维人脸识别技术面临的挑战和未来的发展方向。

关键词: 三维人脸, 人脸识别, 特征提取, 三维人脸数据库

LIU Li, GONG Yong, ZHAO Guoqiang. Research Progress in Three-Dimensional Face Recognition[J]. Computer Engineering and Applications, 2023, 59(23): 28-47.

刘力, 龚勇, 赵国强. 三维人脸识别研究进展[J]. 计算机工程与应用, 2023, 59(23): 28-47.

References

[1] MINAEE S，ABDOLRASHIDI A，SU H，et al.Biometrics recognition using deep learning：a survey[J].Artificial Intelligence Review，2023，56（8）：8647-8695.
[2] KORTLI Y，JRIDI M，FALOU A A，et al.Face recognition systems：a survey[J].Sensors，2020，20（2）：342.
[3] WANG X，PENG J，ZHANG S，et al.A survey of face recognition[J].arXiv：2212.13038，2022.
[4] DALVI J，BAFNA S，BAGARIA D，et al.A survey on face recognition systems[J].arXiv：2201.02991，2022.
[5] DU H，SHI H，ZENG D，et al.The elements of end-to-end deep face recognition：a survey of recent advances[J].ACM Computing Surveys，2022，54（10S）：1-42.
[6] ZHOU S，XIAO S.3D face recognition：a survey[J].Human-centric Computing and Information Sciences，2018，8（1）：1-27.
[7] GUO G，ZHANG N.A survey on deep learning based face recognition[J].Computer Vision and Image Understanding，2019，189：102805.
[8] SHARMA S，KUMAR V.3D face reconstruction in deep learning era：a survey[J].Archives of Computational Methods in Engineering，2022，29（5）：3475-3507.
[9] ZHANG J，LUXIMON Y，SHAH P，et al.3D statistical head modeling for face/head-related product design：a state-of-the-art review[J].Computer-Aided Design，2023，159：103483.
[10] TOPSAKAL O，AKINCI T C，MURPHY J，et al.Detecting facial landmarks on 3D models based on geometric properties-A review of algorithms，enhancements，additions and open-source implementations[J].IEEE Access，2023，11：25593-25603.
[11] MAURIZIO LA CAVA S，ORRù G，GOLDMANN T，et al.3D face reconstruction for forensic recognition-A survey[C]//Proceedings of the 26th International Conference on Pattern Recognition，Montréal，Aug 21-25，2022.Piscataway：IEEE，2022：930-937.
[12] WANG T，ZHANG K，CHEN X，et al.A survey of deep face restoration：denoise，super-resolution，deblur，artifact removal[J].arXiv：2211.02831，2022.
[13] JING Y，LU X，GAO S.3D face recognition：a survey[J].arXiv：2108.11082，2021.
[14] KITTLER J，HILTON A，HAMOUZ M，et al.3D assisted face recognition：a survey of 3D imaging，modelling and recognition approachest[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，San Diego，Sep 21-23，2005.Piscataway：IEEE，2005：114-120.
[15] COLOMBO A，CUSANO C，SCHETTINI R.UMB-DB：a database of partially occluded 3D faces[C]//Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops，Barcelona，Nov 6-13，2011.Piscataway：IEEE，2011：2113-2119.
[16] GAFNI G，THIES J，ZOLLHOFER，et al.Dynamic neural radiance fields for monocular 4D facial avatar reconstruction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：8649-8658.
[17] HAN P，CAI Y，LIU F，et al.Computational polarization 3D：new solution for monocular shape recovery in natural conditions[J].Optics and Lasers in Engineering，2022，151：106925.
[18] MALLIKARJUN B R，TEWARI A，OH T，et al.Monocular reconstruction of neural face reflectance fields[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：4791-4800.
[19] BAI Z，CUI Z，LIU X，et al.Riggable 3D face reconstruction via in network optimization[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：6216-6225.
[20] DIB A，THEBAULT C，AHN J，et al.Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，Montreal，Oct 11-17，2021.Piscataway：IEEE，2021：12819-12829.
[21] WU S，RUPPRECHT C，VEDALDI A.Unsupervised learning of probably symmetric deformable 3D objects from images in the wild[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，Seattle，Jun 16?18，2020.Piscataway：IEEE，2020：1-10.
[22] ZHANG Z，GE Y，TAI Y，et al.Physically-guided disentangled implicit rendering for 3D face modeling[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，New Orleans，Jun 21-24，2022.Piscataway：IEEE，2022：20353-20363.
[23] WEN Y，LIU W，RAJ B，et al.Self-supervised 3D face reconstruction via conditional estimation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision，Montreal，Oct 11-17，2021.Piscataway：IEEE，2021：13289-13298.
[24] JIANG D，JIN Y，ZHANG F L，et al.Sphere face model：a 3D morphable model with hypersphere manifold latent space using joint 2D/3D training[J].Computational Visual Media，2023，9（2）：279-296.
[25] 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.
[26] EGGER B，SMITH W A P，TEWARI A，et al.3D morphable face models—past，present，and future[J].ACM Transactions on Graphics，2020，39（5）：1-38.
[27] TRAN L，LIU X.Nonlinear 3D face morphable model[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Salt Lake City，Jun 18-22，2018.Piscataway：IEEE，2018：7346-7355.
[28] JIN Y，LI Q，JIANG D，et al.High fidelity 3D face reconstruction with multi-scale details[J].Pattern Recognition Letters，2022，153：51-58.
[29] WU C，XU Q，NEUMANN U.Synergy between 3DMM and 3D landmarks for accurate 3D facial geometry[C]//Proceedings of the 2021 International Conference on 3D Vision，London，Dec 1-3，2021.Piscataway：IEEE，2021：453-463.
[30] YANG H，ZHU H，WANG Y，et al.Facescape：a large-scale high quality 3D face dataset and detailed riggable 3D face prediction[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，Seattle，Jun 16-18，2020.Piscataway：IEEE，2020：601-610.
[31] TIWARI H，SUBRAMANIAN V K.Reduced dependency fast unsupervised 3D face reconstruction[C]//Proceedings of the 29th IEEE International Conference on Image Processing，Bordeaux，Oct 16-19，2022.Piscataway：IEEE，2022：1021-1025.
[32] ZHANG Z，GE Y，CHEN R，et al.Learning to aggregate and personalize 3D face from in-the-wild photo collection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：14214-14224.
[33] GENOVA K，COLE F，MASCHINOT A，et al.Unsupervised training for 3D morphable model regression[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Salt Lake City，Jun 18-22，2018.Piscataway：IEEE，2018：8377-8386.
[34] TIWARI H，CHEN M，TSAI Y，et al.Self-supervised robustifying guidance for monocular 3D face reconstruction[J].arXiv：2112.14382，2021.
[35] SHANG J，SHEN T，LI S.Self-supervised monocular 3D face reconstruction by occlusion-aware multi-view geometry consistency[C]//Proceedings of the European Conference on Computer Vision，Glasgow，Aug 23-28，2020.Cham：Springer，2020：53-70.
[36] DENG Y，YANG J，XU S，et al.Accurate 3D face reconstruction with weakly-supervised learning：from single image to image set[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops，Long Beach，Jun 16?20，2019.Piscataway：IEEE，2019：0-0.
[37] TRAN L，LIU X.On learning 3D face morphable model from in-the-wild images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，43（1）：157-171.
[38] JIN B，CRUZ L，GONCALVES N.Pseudo RGB-D face recognition[J].IEEE Sensors Journal，2022，22（22）：21780-21794.
[39] CHIU M，CHENG H，WANG C，et al.High-accuracy RGB-D face recognition via segmentation-aware face depth estimation and mask-guided attention network[C]//Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition，Jodhpur，Dec 15-18，2021.Piscataway：IEEE，2021：1-8.
[40] TEWARI A，PAN X，FRIED O，et al.Disentangled3D：Learning a 3D generative model with disentangled geometry and appearance from monocular images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，New Orleans，Jun 21-24，2022.Piscataway：IEEE，2022：1516-1525.
[41] XU W，XIE X，LAI J.RelightGAN：instance-level generative adversarial network for face illumination transfer[J].IEEE Transactions on Image Processing，2021，30：3450-3460.
[42] WANG C，PENG C.3D face point cloud reconstruction and recognition using depth sensor[J].Sensors，2021，21（8）：2587-2612.
[43] NASSIH B，AMINE A，NGADI M，et al.An efficient three-dimensional face recognition system based random forest and geodesic curves[J].Computational Geometry，2021，97：101758-101765.
[44] ZHANG Z，DA F，YU Y.Data-free point cloud network for 3D face recognition[J].arXiv：1911.04731，2019.
[45] ATIK M E，DURAN Z.Deep learning-based 3D face recognition using derived features from point cloud[C]//Proceedings of the 5th International Conference on Smart City Applications，Karabuk，Oct 7-9，2020.Cham：Springer，2021：797-808.
[46] MOSCHOGLOU S，PLOUMPIS S，NICOLAOU M A，et al.3DFaceGAN：adversarial nets for 3D face representation，generation，and translation[J].International Journal of Computer Vision，2020，128（10）：2534-2551.
[47] BHOPLE A，SHRIVASTAVA A M，PRAKASH S.Point cloud based deep convolutional neural network for 3D face recognition[J].Multimedia Tools and Applications，2021，80：30237-30259.
[48] WANG Q，QIAN W Z，LEI H，et al.Siamese neural Pointnet：3D face verification under pose interference and partial occlusion[J].Electronics，2023，12（3）：620-636.
[49] BHOPLE A R，PRAKASH S.Learning similarity and dissimilarity in 3D faces with triplet network[J].Multimedia Tools and Applications，2021，80：35973-35991.
[50] ZOU H，SUN X.3D face recognition based on an attention mechanism and sparse loss function[J].Electronics，2021，10：2539-2552.
[51] BAHRI M，SULLIVAN E O，GONG S，et al.Shape my face：registering 3D face scans by surface-to-surface translation[J].International Journal of Computer Vision，2021，129（9）：2680-2713.
[52] CAI X，CAO Y，REN Y，et al.Multi-objective evolutionary 3D face reconstruction based on improved encoder-decoder network[J].Information Sciences，2021，581：233-248.
[53] CHANG Y，JUNG C，XU Y.FinerPCN：high fidelity point cloud completion network using pointwise convolution[J].Neurocomputing，2021，460：266-276.
[54] GAO G，YANG H，LIU H.3D point cloud face recognition based on deep learning[J].Journal of Computer Applications，2021，41（9）：2736-2740.
[55] YU C，ZHANG Z，LI H，et al.Meta-learning-based adversarial training for deep 3D face recognition on point clouds[J].Pattern Recognition，2023，134：109065-109075.
[56] LUO C，ZHANG J，BAO C，et al.Robust 3D face modeling and tracking from RGB-D images[J].Multimedia Systems，2022，28：1657-1666.
[57] ZHU X，YANG F，HUANG D，et al.Beyond 3DMM space：Towards ne-grained 3D face reconstruction[C]//Proceedings of the 16th European Conference on Computer Vision，Glasgow，Aug 23-28，2020.Cham：Springer，2020：343-358.
[58] LI P，PEI Y，ZHONG Y，et al.Robust 3D face reconstruction from single noisy depth image through semantic consistency[J].IET Computer Vision，2021，15（6）：393-404.
[59] ZHONG Y，PEI Y，LI P，et al.Face denoising and 3D reconstruction from a single depth image[C]//Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition，Buenos Aires，May 18-22，2020.Piscataway：IEEE，2020：117-124.
[60] DUTTA K，BHATTACHARJEE D，NASIPURI M，et al.Complement component face space for 3D face recognition from range images[J].Applied Intelligence，2021，51（4）：2500-2517.
[61] BOUMEDINE A Y，BENTAIEB S，OUAMRI A.3D face identification based on normal maps[C]//Proceedings of the International Conference on Advances in Communication Technology，Computing and Engineering，Meknes，Jun 24-25，2022.Amsterdam：Elsevier，2022：260-269.
[62] SUI M，ZHU Z，ZHAO F，et al.FFNet-M：feature fusion network with masks for multi-modal facial expression recognition[C]//Proceedings of the 2021 IEEE International Conference on Multimedia and Expo，Shenzhen，Jul 5-9，2021.Piscataway：IEEE，2021：1-6.
[63] WANG L，CHEN Z，YU T，et al.Faceverse：a fine-grained and detail-controllable 3D face morphable model from a hybrid dataset[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，New Orleans，Jun 21-24，2022.Piscataway：IEEE，2022：20333-20342.
[64] DUTTA K，BHATTACHARJEE D，NASIPURI M.SpPCANet：a simple deep learning-based feature extraction approach for 3D face recognition[J].Multimedia Tools and Applications，2020，79：31329-31352.
[65] LIN S，JIANG C，LIU F，et al.High quality facial data synthesis and fusion for 3D low-quality face recognition[C]//Proceedings of the 2021 IEEE International Joint Conference on Biometrics，Shenzhen，Aug 4?7，2021.Piscataway：IEEE，2021：1-8.
[66] UPPAL H.Attention and depth hallucination for RGB-D face recognition with deep learning[D].Kingston：Queen’s University，2021.
[67] CHIU M T，CHENG H Y，WANG C Y，et al.RGB-D face recognition with identity-style disentanglement and depth augmentation[J].IEEE Transactions on Biometrics，Behavior，and Identity Science，2023，5（3）：334-347.
[68] JIANG L，ZHANG J，LI C，et al.RGB-D face recognition via spatial and channel attentions[C]//Proceedings of the 2021 IEEE 5th Advanced Information Technology，Electronic and Automation Control Conference，Chongqing，Mar 12-14，2021.Piscataway：IEEE，2021：2037-2041.
[69] ZHU Y，GAO J，WU T，et al.Exploiting enhanced and robust RGB-D face representation via progressive multi-modal learning[J].Pattern Recognition Letters，2023，166：38-45.
[70] NETO J B C，FERRARI C，MARANA A N，et al.Learning streamed attention network from descriptor images for cross-resolution 3D face recognition[J].ACM Transactions on Multimedia Computing，Communications and Applications，2023，19（1S）：1-20.
[71] UPPAL H，MOGHADDAM A S，GREENSPAN M，et al.Two-level attention-based fusion learning for RGB-D face recognition[C]//Proceedings of the 25th International Conference on Pattern Recognition，Milan，Jan 10?15，2021.Piscataway：IEEE，2021：10120-10127.
[72] ZHANG F，LIU N，CHANG L，et al.Edge-guided single facial depth map super-resolution using CNN[J].IET Image Processing，2020，14（17）：4708-4716.
[73] LEE J，BHATTARAI B，KIM T.Face parsing from RGB and depth using cross- domain mutual learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：1501-1510.
[74] GHOSH S，SINGH R，VATSA M，et al.RGB-D face recognition using reconstruction based shared representation[C]//Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition，Jodhpur，Dec 15-18，2021.Piscataway：IEEE，2021：1-8.
[75] ZHAO P，MING Y，MENG X，et al.LMFNet：a lightweight multiscale fusion network with hierarchical structure for low-quality 3D face recognition[J].IEEE Transactions on Human-Machine Systems，2022，53（1）：239-252
[76] ZHENG H，WANG W，WEN F，et al.A complementary fusion strategy for RGB-D face recognition[C]//Proceedings of the 28th International Conference on Multimedia Modeling，Qui Nhon，Jun 6?10，2022.Cham：Springer，2022：339-351.
[77] LI H，SUI M，ZHU Z，et al.MFEVit：a robust lightweight transformer-based network for multi-modal 2D+3D facial expression recognition[J].arXiv：2109.13086，2021.
[78] XIAO M，YI H，HUANG Y，et al.Effective key region-guided face detail optimization algorithm for 3D face reconstruction[J].Journal of Sensors，2022，2022：1-13.
[79] PETKOVA R，MANOLOVA A，TONCHEV K，et al.3D face reconstruction and verification using multi-view RGB-D data[C]//Proceedings of the 2022 Global Conference on Wireless and Optical Technologies，Malaga，Feb 14-17，2022.Piscataway：IEEE，2022：1-6.
[80] JIANG L，ZHANG J，DENG B.Robust RGB-D face recognition using attribute-aware loss[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，42（10）：2552-2566.
[81] SINGH J M，RAMACHANDRA R.3D face morphing attacks：generation，vulnerability and detection[J].IEEE Transactions on Biometrics，Behavior，and Identity Science，2023：1-15.doi：10.1109/TBIOM.2023.3324684.
[82] GU Y，YAN H，ZHANG X，et al.3D facial expression recognition via attention-based multichannel data fusion network[J].IEEE Transactions on Instrumentation and Measurement，2021，70：1-10.
[83] DEVI P R，BASKARAN R.SL2E-AFRE：personalized 3D face reconstruction using autoencoder with simultaneous subspace learning and landmark estimation[J].Applied Intelligence，2021，51（4）：2253-2268.
[84] XIAO Q，WU Y，WANG D，et al.Beauty3DFaceNet：deep geometry and texture fusion for 3D facial attractiveness prediction[J].Computers and Graphics，2021，98：11-18.
[85] TENG W，BAI C.Unimodal face classification with multi-modal training[C]//Proceedings of the 16th IEEE International Conference on Automatic Face and Gesture Recognition，Jodhpur，Dec 15-18，2021.Piscataway：IEEE，2021：1-5.
[86] NIU W，ZHAO Y，YU Z，et al.Research on a face recognition algorithm based on 3D face data and 2D face image matching[J].Journal of Visual Communication and Image Representation，2023，91：103757-103767.
[87] GUO Y，CAI L，ZHANG J.3D face from X：learning face shape from diverse sources[J].IEEE Transactions on Image Processing，2021，30：3815-3827.
[88] YANG H，ZHU K，HUANG D，et al.Intensity enhancement via GAN for multi-modal face expression recognition[J].Neurocomputing，2021，454：124-134.
[89] SUI M，LI H，ZHU Z，et al.AFNet-M：adaptive fusion network with masks for 2D+3D facial expression recognition[C]//Proceedings of the 30th IEEE International Conference on Image Processing，Kuala Lumpur，Oct 8-11，2023.Piscataway：IEEE，2023：116-120.
[90] TALAB M A，QAHRAMAN N A，AFTAN M M，et al.Local feature methods based facial recognition[C]//Proceedings of the 2022 International Congress on Human-Computer Interaction，Optimization and Robotic Applications，Ankara，Jun 9-11，2022.Piscataway：IEEE，2022：1-5.
[91] LIU F，ZHAO Q，LIU X，et al.Joint face alignment and 3D face reconstruction with application to face recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2018，42（3）：664-678.
[92] THAREWAL S，MALCHE T，TIWARI P K，et al.Score-level fusion of 3D face and 3D ear for multimodal biometric human recognition[J].Computational Intelligence and Neuroscience，2022，2022：1-9.
[93] FANG H S，XIE S Q，TAI Y W，et al.RMPE：regional multi-person pose estimation[C]//Proceedings of the IEEE International Conference on Computer Vision，Venice，Oct 22-29，2017.Piscataway：IEEE，2017：2334-2343.
[94] KREISS S，BERTONI L，ALAHI A.Openpifpaf：composite fields for semantic key-point detection and spatio-temporal association[J].IEEE Transactions on Intelligent Transportation Systems，2021，23（8）：13498-13511.
[95] CHEN Q，GE T，XU Y，et al.Semantic human matting[C]//Proceedings of the 26th ACM International Conference on Multimedia，Seoul，Oct 22-26，2018.New York：ACM，2018：618-626.
[96] ZHAO J，LI J，CHENG Y，et al.Understanding humans in crowded scenes：deep nested adversarial learning and a new benchmark for multi-human parsing[C]//Proceedings of the 26th ACM International Conference on Multimedia，Seoul，Oct 22-26，2018.New York：ACM，2018：792-800.
[97] SHARMA S，KUMAR V.Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning[J].Multimedia Tools and Applications，2020，79：17303-17330.
[98] SHARMA S，KUMAR V.Voxel-based 3D occlusion-invariant face recognition using game theory and simulated annealing[J].Multimedia Tools and Applications，2020，79：26517-26547.
[99] SHARMA S，KUMAR V.3D landmark-based face restoration for recognition using variational autoencoder and triplet loss[J].IET Biometrics，2021，10（1）：87-98.
[100] YI D，LEI Z，LIAO S，et al.Learning face representation from scratch[J].arXiv：1411.7923，2014.
[101] SENGUPTA S，CHEN J，CASTILLO C，et al.Frontal to profile face verification in the wild[C]//Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision，Lake Placid，Mar 7-10，2016.Piscataway：IEEE，2016：1-9.
[102] KUMAR N，BERG A C，BELHUMEUR P N，et al.Attribute and simile classifiers for face verification[C]//Proceedings of the 12th IEEE International Conference on Computer Vision，Kyoto，Sep 29-Oct 2，2009.Piscataway：IEEE，2009：365-372.
[103] GAO W，CAO B，SHAN S，et al.The CAS-PEAL large-scale Chinese face database and baseline evaluations[J].IEEE Transactions on Systems，Man，and Cybernetics-Part A：Systems and Humans，2007，38（1）：149-161.
[104] ZHU Z，HUANG G，DENG J，et al.Webface260m：a benchmark unveiling the power of million-scale deep face recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway：IEEE，2021：10492-10502.
[105] HUANG G B，LEARNED-MILLER G.Labeled faces in the wild：updates and new reporting procedures[R].University of Massachusetts，2014：1-5.
[106] YANG S，LUO P，LOY C C，et al.Wider face：a face detection benchmark[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，Las Vegas，Jun 27-30，2016.Piscataway：IEEE，2016：5525-5533.
[107] KLARE B F，KLEIN B，TABORSKY E，et al.Pushing the frontiers of unconstrained face detection and recognition：IARPA Janus Benchmark A[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition，Boston，Jun 7-12，2015.Piscataway：IEEE，2015：1931-1939.
[108] LIU Z，LUO P，WANG X，et al.Deep learning face attributes in the wild[C]//Proceedings of the IEEE International Conference on Computer Vision，Santiago，Dec 7-13，2015.Piscataway：IEEE，2015：3730-3738.
[109] KOESTINGER M，WOHLHART P，ROTH P M，et al.Annotated facial landmarks in the wild：a large-scale，real-world database for facial landmark localization[C]//Proceedings of the IEEE International Conference on Computer Vision，Barcelona，Nov 6-13，2011.Piscataway：IEEE，2011：2144-2151.
[110] 言有三.深度学习之人脸图像处理[M].北京：机械工业出版社，2020：145-162.
YAN Y S.Deep learning for face image processing[M].Beijing：China Machine Press，2020：145-162.
[111] BELHUMEUR P N，JACOBS D W，KRIEGMAN D J，et al.Localizing parts of faces using a consensus of exemplars[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2013，35（12）：2930-2940.
[112] LE V，BRANDT J，LIN Z，et al.Interactive facial feature localization[C]//Proceedings of the 12th European Conference on Computer Vision，Florence，Oct 7-13，2012.Cham：Springer，2012：679-692.
[113] ZHANG Y，GUO Z，LIN Z，et al.The NPU multi-case Chinese 3D face database and information processing[J].Chinese Journal of Electronics，2012，21（2）：283-286.
[114] MORENO A.GavabDB：a 3D face database[C]//Proceedings of the 2nd COST275 Workshop on Biometrics on the Internet，Vigo，Mar 25-26，2004.Luxembourg：EU Publications Office，2004：75-80.
[115] VELTKAMP R C，DRIRA H，AMOR B B，et al.SHREC’11 track：3D face models retrieval[C]//Proceedings of the 4th Eurographics Workshop on 3D Object Retrieval，Wales，Apr 10，2011.Goslar：Eurographics Association Postfach，2011：89-95.
[116] BAGDANOV A D，DEL BIMBO A，MASI I.The Florence 2D/3D hybrid face dataset[C]//Proceedings of the 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding，Scottsdale，Nov 28-Dec 1，2011.New York：Association for Computing Machinery，2011：79-80.
[117] HESELTINE T，PEARS N，AUSTIN J.Three-dimensional face recognition using combinations of surface feature map subspace components[J].Image and Vision Computing，2008，26（3）：382-396.
[118] YIN B，SUN Y，WANG C，et al.BJUT-3D large scale 3D face database and information processing[J].Journal of Computer Research and Development，2009，6：1009-1018.
[119] SAVRAN A，ALYUZ N，DIBEKLIOGLU H，et al.Bosphorus database for 3D face analysis[C]//Proceedings of the Biometrics and Identity Management：First European Workshop，Roskilde，May 7-9，2008.Cham：Springer，2008：47-56.
[120] GILANI S Z，MIAN A.Learning from millions of 3D scans for large-scale 3D face recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Salt Lake City，Jun 18-22，2018.Piscataway：IEEE，2018：1896-1905.
[121] GOSWAMI G，BHARADWAJ S，VATSA M，et al.On RGB-D face recognition using Kinect[C]//Proceedings of the 2013 IEEE 6th International Conference on Biometrics：Theory，Applications and Systems，Washington，Sep 29-Oct 2.Piscataway：IEEE，2013：1-6.
[122] VIJAYAN V，BOWYER K W，FLYNN P J，et al.Twins 3D face recognition challenge[C]//Proceedings of the 2011 International Joint Conference on Biometrics，Washington，Oct 11-13，2011.Piscataway：IEEE，2011：1-7.
[123] GUPTA S，CASTLEMAN K R，MARKEY M K，et al.Texas 3D face recognition database[C]//Proceedings of the 2010 IEEE Southwest Symposium on Image Analysis & Interpretation，Austin，May 23-25，2010.Piscataway：IEEE，2010：97-100.
[124] PHILLIPS P J，FLYNN P J，SCRUGGS T，et al.Overview of the face recognition grand challenge[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition，San Diego，Sep 21-23，2005.Piscataway：IEEE，2005：947-954.
[125] ZHANG J，HUANG D，WANG Y，et al.Lock3Dface：a large-scale database of low-cost kinect 3D faces[C]//Proceedings of the 2016 International Conference on Biometrics，Halmstad，Jun 13-16，2016.Piscataway：IEEE，2016：1-8.
[126] URBANOVá P，FERKOVá Z，JANDOVá M，et al.Introducing the FIDENTIS 3D face database[J].Anthropological Review，2018，81（2）：202-223.
[127] MIN R，KOSE N，DUGELAY J.KinectFaceDB：a Kinect database for face recognition[J].IEEE Transactions on Systems，Man，and Cybernetics：Systems，2014，44（11）：1534-1548.
[128] CHENG S，KOTSIA I，PANTIC M，et al.4DFAB：a large scale 4D database for facial expression analysis and biometric applications[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，Salt Lake City，Jun 18-22，2018.Piscataway：IEEE，2018：5117-5126.
[129] WANG Z，BOVIK A C，SHEIKH H R，et al.Image quality assessment：from error visibility to structural similarity[J].IEEE Transactions on Image Processing，2004，13（4）：600-612.
[130] CUI J，HAN H，SHAN S，et al.RGB-D face recognition：a comparative study of representative fusion schemes[C]//Proceedings of the 13th Chinese Conference，Chinese Conference on Biometric Recognition，ürümqi，Aug 11-12，2018.Cham：Springer，2018：358-366.