摘要
自动人脸识别在公共安全、智能监控、数字身份认证、电子商务、多媒体和数字娱乐等领域具有巨大的应用价值,同时,人脸识别的研究涉及多个学科,具有重要的理论研究价值,受到各国政府、科研单位以及军事、安全、情报部门的广泛关注和高度重视。经过几十年的研究,人脸识别取得了长足的发展与进步,目前在控制和配合条件下,人脸图像识别可以取得比较高的识别率,在一定场合下,已经可以应用到人们的生活中;但是在非控制条件和非配合条件下的人脸图像识别仍然是一个极具挑战性的课题。由于人脸识别的广泛应用,ISO/IEC成立工作组制定相关的标准,其中之一就是评估对系统性能造成影响的各种因素。
2006年,中科院自动化所生物识别与安全技术研究中心代表中国向ISO/IEC标准委员会提交关于人脸图像质量的标准草案,该标准与2007年2月确立初稿,经修改,讨论与2007年5月8号通过。本文作者作为该标准的重要参与者之一,参与了该标准的起草修改过程,本文对该标准的做了详细介绍并对提出了一种新的基于人脸对称性的非对称光照和姿态评估方法。
在人脸图像质量标准提交之前,指纹图像标准已经被公布,由于指纹是一种接触式的生物特征,采集图像的条件大都可以控制,因此指纹图像质量评估更注重特征的有效性,因此标准的制定相对比较直观;但对于人脸识别,由于人脸属于非接触性生物识别,这也造成了影响人脸图像质量的因素的多样性,如何确立标准对人脸图像的各个因素进行评估是相对很困难的。此外,对于一幅人脸图像,如何给出一个总体的评价,尚无相关的理论和算法,这也对标准的确立带来很多麻烦。针对这一问题,本文提出人脸图像质量评估的标准方法框架。本文首先定义质量评估方面的一些概念,然后对影响图像质量的各种因素做了总结和分类。针对各个影响图像质量的因素,本文给出了相关的评估方法。对于图像的整体特性,如全局光照,对比度等,前人已做了相关的评估研究,本文主要总结了一些评价这些因素的依据,从而为以后的算法确立一定的参考和依据,这些依据也得到的国际专家的认可,已被收录到ISO/IEC国际标准中。本文对这些因素对识别系统的影响作了定量的实验。鉴于影响人脸图像质量因素的多样性,且相关的评估算法均为对单一因素做评估的现状,最后,本文采用了两种分数:各因素的单一质量分数和图像的整体分数来对图像质量进行量化的表示。
前人的研究表明,光照和姿态是影响人脸识别系统的两个最为重要的因素,虽然针对这两个问题,人们提出了很多算法来削弱光照和姿态对识别系统的影响,但始终没有很大的突破,无法从根本上解决这一问题,因此,ISO/IEC在制定人脸图像标准时也需要为这两个问题作出全面客观评估。针对这一问题,本文提出采用人脸的对称性这一人脸的统计特征来评估非对称光照和不正确姿态,为了更好的描述人脸的对称性,本文借鉴于LBP子窗口的思想,采用局部子窗口来描述人脸左右半边的对称性。文中给出了对称性的评价公式,然后根据该公式对光照和姿态进行了初步的评估,该方法已被收录到ISO相关的标准中。
ISO相关标准指出,生物图像质量评估的最终目的是最大化图像质量与系统性能即匹配引擎输出之间的联系,由于目前并没有一个标准的带有图像质量的数据库。所以大多的图像质量评估算法的标准质量均有人为来划定,这并不符合图像质量评估的目的。本文针对这一问题,首先通过实验证明,采用人为判定的图像质量与系统输出的匹配分数并没有线性的关系,即图像质量高并不意味着图像的正确匹配分数高(所谓正确匹配分数就是同一个人的两幅图像匹配产生的分数),而图像质量低也不意味着图像的正确匹配分数低。作为改进,本文提出采用回归的方法在图像与匹配分数之间建立联系,木文研究了两种回归方法,一种是传统的线性回归,一种是基于boosting的非线性回归方法。木文采用训练测试的机制,首先通过训练在图像和匹配分数之间建立模型,然后通过测试验证该模型的正确性,实验证明,本文提出的方法可以有效的预测人脸图像的匹配分数,这也对以后改进系统性能提供了一个很有意义的基础。
Automatic face recognition has great potential applications in public security, intelligent surveillance, digital personal identify, electronic commerce, multimedia, digital entertainment, etc. and has great theory value in many subjects, so face recognition has attracted much research attention from the research institutes, governments, military and security departments. Over the past 30 years, great progress and developments have been made in face recognition. Now under the controlled and cooperative conditions, face recognition systems perform very well, but under uncontrolled and uncooperative conditions, especially when the illumination in face images and facial poses are variant. For the wide use of face recognition, ISO/IEC established a group to draft standards about face image. One of them is to evaluate the aspects which influent system performance.
In 2006, CBSR(Center for Biometrics and Security Research) summit a standard draft on face image quality to ISO/IEC on behalf of China. In February 2007, the working draft was published. After some discussion and modification, the standard was passed in May 8. 2007. The author of this article, which is partner of this standard, will give a detailed introduction on this standard in this article. Also this paper proposes some algorithms to evaluate non-frontal lighting and improper facial pose.
Before the standard was summit, some standards about fingerprint quality were established. Fingerprint is a touchable biometric feature, the conditions of capturing fingerprint image are under control. So fingerprint image quality evaluation is mostly about the effect of features. But for face recognition, the aspects that influent image quality are diversify because face recognition is a un-touchable biometric feature. So how to establish a standard for face image is difficult. On the other hand, for a face image, how to give an overall evaluation, there are no research on this problem. For this problem, this paper proposes a standard framework to evaluation the aspects that affect face image quality. First, some definition about quality evaluation are defined. Then we classify these aspects into some class. For most of the aspects, we give the evaluation methods in this paper. This gives a basement for future work on face image quality evaluation. Until now, most evaluation algorithm is to evaluation one aspects, so we propose an aspects-score and over-score method to give face image an overall evaluation.
Based on the past research, non-frontal lighting and improper facial pose are the most important aspects that affect the system performance. For these two aspects, researchers proposes many algorithms to weaken the influence of them, but no big progress was made. These two problem still have not been solved. So the standard need to give evaluation for these problems. For this reason, this paper propose a method which use symmetry, a statistical feature of face to evaluate non-frontal lighting and improper facial pose. To describe symmetry, this paper use local windows to evaluate the symmetry of face image. This idea is came from LBP. After evaluation of symmetry, we give the methods to assess non-frontal lighting and improper facial pose.
From the ISO stands, image quality must be directly connected with matching performance. But until now, there are no database with standard image quality. So most image quality methods used the quality marked by hand, this is not proper. For the problem of image quality can't been directly connected with matching performance, this paper uses regression method to build models between face image and genuine matching score. For the face images with non-frontal lighting and pose variation, first we evaluate the input image's symmetry, then build models between face image symmetry and matching score. Two regression methods are discussed, the first one, which selects some most effective features using Adaboost, build a linear model between these features and matching score. The second one is a non-linear method based on boosting. The experiment result show that these two methods can predict matching score very well.
引文
[1]W.Zhao,R.Chellappa,PJ Phillips,and A.Rosenfeld.Face Recognition:A Literature Survey.ACM Computing Surveys,35(4):399-458,2003.
[2]H.Chan and W.Bledsoe."A man-machine facial recognition system:some preliminary results".Technical report,Panoramic Research Inc,1965.
[3]T.Kanade.Picture processing system by computer complex and recognition of human faces.Department of Information Science.Kyoto University.
[4]M.D.Kelly.VISUAL IDENTIFICATION OF PEOPLE BY COMPUTER.1970.
[5]M.H.Yang,D.J.Kriegman,and N.Ahuja.Detecting Faces in Images:A Survey.2002.
[6]G.Yang and T.S.Huang.Human face detection in a complex background.Pattern Recognition,27(1):53-63,1994.
[7]M.H.Yang,D.Roth,and N.Ahuja.A SNoW-Based Face Detector.Urbana,51:61801.
[8]K.C.Yow and R.Cipolla.Feature-based human face detection.Image and Vision Computing,15(9):713-735,1997.
[9]Y.Dai and Y.Nakano.Face-texture model based on SGLD and its application in face detection in a color scene.Pattern Recognition,29(6):1007-1017,1996.
[10]R.Brunelli and T.Poggio.Face recognition:features versus templates.Pattern Analysis and Machine Intelligence,IEEE Transactions on,15(10):1042-1052,1993.
[11]A.L.Yuille.Deformable Templates for Face Recognition.Journal of Cognitive Neuroscience,3(1):59-70,1991.
[12]M.H.Yang and N.Ahuja.Detecting human faces in color images.Image Processing.1998.ICIP 98.Proceedings.1998 International Conference on,1,1998.
[13]V.Govindaraju.Locating human faces in photographs.International Journal of Computer Vision,19(2):129-146,1996.
[14]J.Wang and T.Tan.A new face detection method based on shape information.Pattern Recognition Letters,21(6-7):463-471,2000.
[15]H.Wu,Q.Chen,and M.Yachida.Face Detection From Color Images Using a Fuzzy Pattern Matching Method.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 557-563,1999.
[16]J.Cai and A.Goshtasby.Detecting human faces in color images,hnage and Vision Computing,18(1):63-75,1999.
[17]M.Turk and A.Pentland.Eigenfaces for Recognition.Journal of Cognitive Neuroscience,3(1):71-86,1991.
[18]T.Poggio and K.K.Sung.Finding human faces with a gaussian mixture distribution-based face model.Asian Conf.on Computer Vision,pages 435-440,1995.
[19]K.K.Sung.Learning and Example Selection for Object and Pattern Detection.1996.
[20]K.K.Sung and T.Poggio.Example-based learning for view-based human face detection.Pattern Analysis and Machine Intelligence,IEEE Transactions on,20(1):39-51,1998.
[21]H.A.Rowley,S.Baluja,and T.Kanade.Rotation invariant neural network-based face detection.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,pages 38-44,1998.
[22]P.Juell and R.Marsh.A hierarchical neural network for human face detection.Pattern Recognition,29(5):781-787,1996.
[23]E.Osuna,R.Freund,F.Girosi,et al.Training support vector machines:an application to face detection.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,24,1997.
[24]B.Heisele,T.Poggio,and M.Pontil.Face detection in still gray images.CBCL Paper,187.
[25]P.Wang and Q.Ji.Multi-View Face Detection under Complex Scene based on Combined SVMs.International Conference on Pattern Recognition,4:179-182,2004.
[26]P.Viola and M.Jones.Rapid object detection using a boosted cascade of simple features.Proc.CVPR,1:511-518,2001.
[27]S.Z.Li,Z.Q.Zhang,H.Y.Shum,and H.J.Zhang.FloatBoost Learning for Classification.log,1:2.
[28]S.Z.Li and Z.Q.Zhang.FloatBoost Learning and Statistical Face Detection.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 1112-1123,2004.
[29]R.Lienhart and J.Maydt.An extended set of Haar-like features for rapid object detection.Image Processing.2002.Proceedings.2002 International Conference on,1,2002.
[30]C.Huang,H.Ai,Y.Li,and S.Lao.Learning Sparse Features in Granular Space for MultiView Face Detection.Proc.Seventh Int'1 Conf.Automatic Face and Gesture Recognition,pages 401-406,2006.
[31]B.Froba and A.Ernst.Face detection with the modified census transform.Automatic Face and Gesture Recognition,2004.Proceedings.Sixth IEEE International Con.ference on,pages 91-96,2004.
[32]L.Zhang,R.Chu,S.Xiang,S.Liao,and S.Z.Li.Face Detection Based on Multi-Block LBP Representation.LECTURE NOTES IN COMPUTER SCIENCE,4642:11,2007.
[33]T.Mita,T.Kaneko,and O.Hori.Joint Haar-like Features for Face Detection.Proc.ICCV05,2:1619-1626,2005.
[34]B.Wu,H.Ai,and C.Huang.LUT-Based Adaboost for Gender Classification.4th International Conference on Audio and Video-based Biometric Person Authentication,pages 104-110,2003.
[35]R.Xiao,L.Zhu,and H.J.Zhang.Boosting chain learning for object detection.Computer Vision,2003.Proceedings.Ninth IEEE International Conference on,pages 709-715,2003.
[36]C.Huang,H.Ai,Y.Li,and S.Lao.Vector boosting for rotation invariant multi-view face detection.Proceedings of the IEEE International Conference on Computer Vision(ICCV),pages 446-453,2005.
[37]C.Huang,H.Ai,Y.Li,and S.Lao.High-Performance Rotation Invariant Multiview Face Detection.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 671-686,2007.
[38]R.Brunelli and T.Poggio.Face Recognition through Geometrical Features.Proceedings of the Second European Conference on Computer Vision,pages 792-800,1992.
[39]MA Turk and AP Pentland.Face recognition using eigenfaces.Computer Vision and Pattern Recognition,1991.Proceedings CVPR'91.,IEEE Computer Society Conference on,pages 586-591,1991.
[40]K.A.Kim,S.Y.Oh,and H.C.Choi.Facial feature extraction using PCA and wavelet multiresolution images.Automatic Face and Gesture Recognition,2004.Proceedings.Sixth IEEE International Conference on,pages 439-444,2004.
[41]A.L.Yuille,P.W.Hallinan,and D.S.Cohen.Feature extraction from faces using deformable templates.International Journal of Computer Vision,8(2):99-111,1992.
[42]L.Wiskott,J.M.Fellous,N.Kuiger,and C.vonder Malsburg.Face recognition by elastic bunch graph matching.IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775-779,1997.
[43] A. Tefas, C. Kotropoulos, and I. Pitas. Using Support Vector Machines to Enhance the Performance of Elastic Graph Matching for Frontal Face Authentication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pages 735-746,2001.
[44] T.F. Cootes, C.J. Taylor, D.H. Cooper, J. Graham, et al. Active shape models-their training and application. Computer Vision and Image Understanding, 61(1 ):38-59, 1995.
[45] GJ Edwards, TF Cootes, and CJ Taylor. Advances in active appearance models. Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, 1, 1999.
[46] T.F. Cootes, G.J. Edwards, and CJ. Taylor. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):681-685, 2001.
[47] L. Chen, L. Zhang, L. Zhu, M. Li, and H. Zhang. A Novel Facial Feature Localization Method Using Probabilistic-like Output. Asian Conference on Computer Vision, pages 1-10, 2004.
[48] Y. Ma, X. Ding, Z. Wang, and N. Wang. Robust precise eye location under probabilistic framework. Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pages 339-344, 2004.
[49] R.W. Lienhart, L. Liang, and A. Kuranov. Detector tree of boosted classifiers for real-lime object detection and tracking, April 10 2007. US Patent 7,203,669.
[50] D. Cristinacce and T. Cootes. Facial feature detection using adaboost with shape constraints. British Machine Vision Conference, 1:231-240, 2003.
[51] B. Scholkopf, A. Smola, and K.R. Muller. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation, 10(5): 1299-1319, 1998.
[52] PN Belhumeur, JP Hespanha, and DJ Kriegman. Eigenfaces vs. Fisherfaces: recognition using class specific linearprojection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):711-720, 1997.
[53] W. Zhao, R. Chellappa, and A. Krishnaswamy. Discriminant analysis of principal components for face recognition. Automatic Face and Gesture Recognition, 1998. Proceedings.Third IEEE International Conference on, pages 336-341, 1998.
[54] H. Yu and J. Yang. A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recognition, 34(10):2067-2070, 2001.
[55] L.F. Chen, H.Y.M. Liao, M.T. Ko, J.C. Lin, and G.J. Yu. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, 33(10): 1713- 1726,2000.
[56]B.Moghaddam,C.Nastar,and A.Pentland.A bayesian similarity measure for direct image matching.Proceedings of ICPR,96:350.
[57]B.Moghaddam,T.Jebara,and A.Pentland.Bayesian face recognition.Pattern Recognition,33(11):1771-1782,2000.
[58]P.C.Yuen and JH Lai.Face representation using independent component analysis.Pattern Recognition,35(6):1247-1257,2002.
[59]P.S.Penev and J.J.Atick.Local feature analysis:a general statistical theory for object representation.Network:Computation in Neural Systems,7(3):477-500,1996.
[60]T.Ojala,M.Pietikainen,and D.Harwood.A comparative study of texture measures with classification based on featured distributions.Pattern Recognition,29(1):51-59,1996.
[61]T.Ahonen,A.Hadid,and M.Pietikainen.Face recognition with local binary patterns.European Conference on Computer Vision(ECCV),2004.
[62]S.Shan,P.Yang,X.Chen,and W.Gao.AdaBoost Gabor Fisher Classifier tbr Face Recognition.Proc.of IEEE International Workshop on Analysis and Modeling of Faces and Gestures,pages 278-291,2005.
[63]P.Y.S.S.W.Gao and SZ Li.Face recognition using Ada-Boosted Gabor features.Automatic Face and Gesture Recognition,2004.Proceedings.Sixth IEEE International Conference on,pages 356-361,2004.
[64]L.Zhang,SZ Li,Z.Y.Qu.and X.Huang.Boosting Local Feature Based Classifiers for Face Recognition.Computer Vision and Pattern Recognition Workshop,2004 Conference on,pages 87-87,2004.
[65]S.Liao,Z.Lei,X.X.Zhu,Z.Sun,S.Z.Li,and T.Tan.Face Recognition Using Ordinal Features.Proceedings of International Conference on Biometric,2005.
[66]包桂秋and林喜荣.基于人体生物特征的身份鉴别技术发展概况.清华大学学报:自然科学版,41(004):72-76,2001.
[67]C.J.Tilton.Biometric Standards-An Overview.Information Security Technical Report,7(4):36-48,2002.
[68]B.S.Version.1.1(ANS//INCITS 358-2002)",BioAPI Cosortium.
[69]FL Podio,JS Dunn,L.Reinert,et al.Common Biornetric Exchange Format Framework.NISTIR 6259A,NIST,2004.
[70]ISO/IEC JTC 1/SC 37 Standing Document 2.Harmonized Biometric Vocabulary,2005.
[71]GA/T 144 1996.指纹专业名词术语.
[72]GA/T 425 2003.指纹自动识别系统基础技术规范.
[73]ISO/IEC FCD 19785-1.Commnon Biometric Exchange Formats Framework Part Ⅰ:Data element specification.
[74]ANSI/INCITS 377-2004.Finger Pattern Data Interchange Formate.
[75]ANSI/INCITS 378-2004.Finger Minutiae Formate for Data Interchange.
[76]ANSI/INCITS 381-2004.Finger Image Based Interchage Foramte.
[77]ANSI/INCITS 379-2004.Iris Image Interchage Foramte.
[78]ANSI/INCITS 385-2004.Face Recognition Formate for Data Interchage.
[79]ISO/IEC FCD 19784-1.BioAPI-Biometric Application Programming Interface-Part Ⅰ:BioAPI Specification.
[80]ISO 7816-11:2004.Identification cards-Integrated circuit cards-Part Ⅰ:Personal verification through biometric methods.
[81]TM Java Card.Biometdc API White Paper(Working Document).Biometric Consortium Interoperability,Assurance,and PerformanceWorking Group,7:02-0016,2002.
[82]ANSI X9.84-2003.Biometric Information Management and Security.
[83]ISO/IEC WD 19792.A framework for security evaluation and testing of biometric technology.
[84]ISO/IEC WD 24713-1.Biometric Profiles for Interoperability and Data Interchange-Part Ⅰ:Biometric reference Architecture.
[85]ISO/IEC CD 19795-1.Biometric Performance Testing and Reporting Part 1:Principles and Framework.
[86]ISO/IEC WD 24714.Technical Report on Cross Jurisdictional and Societal Aspects of Implementations of Biometric Technologies.
[87]ISO/IEC JTC 1/SC 37 N 1511."Proposed Draft Amendment to ISO/IEC 19794-5 Face Image Data on Conditions for Taking Pictures".March 1,2006.
[88]ISO/IEC JTC 1/SC 37 N 1477.Biometric Sample Quality-Part 5:Face Image Data Sample Quality(Working Draft for comment).February 12,2007.
[89]ISO/IEC JTC 1/SC 37 N 1477.Biometric Sample Quality Standard-Part 1:Framework. January 30, 2006.
[90] ISO/IEC JTC 1/SC 37 N 1760. Biometric Sample Quality - Part 4: Fingerprint Sample Quality. August 21,2006.
[91 ] Athinodoros S. Georghiades and Peter N.Belhumeur. "From few to many: Illumination cone models for face recognition under variable lighting and pose". IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):643-660, 2001.
[92] C. Liu, WT Freeman, R. Szeliski, and S.B. Kang. Noise Estimation from a Single Image. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 1,2006.
[93] J. Yang, H. Choi, and T. Kim. Noise estimation for blocking artifacts reduction in DCT codedimages. Circuits and Systems for Video Technology, IEEE Transactions on, 10(7):1116-1120,2000.
[94] R. Gross, J. Shi, and J. Cohn. "Quo vadis face recognition? - the current state of the art in face recognition". Technical Report TR-01-17, Robotics Institute, Carnegie Mellon University,June 2001.
[95] V. Govindaraju, DB Sher, RK Srihari, and SN Srihari. Locating human faces in newspaper photographs. Computer Vision and Pattern Recognition, 1989. Proceedings CVPR'89., IEEE Computer Society Conference on, pages 549-554, 1989.
[96] J. Miao, B. Yin, K. Wang, L. Shen, and X. Chen. A hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template.Pattern Recognition, 32(7): 1237-1248, 1999.
[97] J. Buhmann, M. Lades, and F. Eeckman. A silicon retina object recognition. Technical report, Technical Report.
[98] M. Lades, JC Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, RP Wurtz, and W. Konen. Distortion invariant object recognition in the dynamic linkarchitecture. Computers,IEEE Transactions on, 42(3):300-311,1993.
[99] S. Edelman, D. Reisfeld, and Y. Yeshurun. A System for Face Recognition that Learns from Examples. Proc. European Conf. Computer Vision, pages 787-791.
[100] H.F. Chen, P.N. Belhumeur, and D.W. Jacobs. In search of illumination invariants. PROC IEEE COMPUTSOC CONF COMPUT VISION PATTERN RECOGNIT, 1:254-261, 2000.
[101] D. Reisfeld and Y. Yeshurun. Robust detection of facial features by generalized symmetry. Pattern Recognition, 1992. Vol. 1. Conference A: Computer Vision and Applications,
Proceedings.,11th IAPR International Conference on,pages 117-120.
[102]Y.Adini,Y.Moses,and S.Ullman.Face Recognition:The Problem of Compensating for Changes in Illumination Direction.1997.
[103]Y.Moses,Y.Adini,and S.Ullman.Face recognition:the problem of compensating for illumination changes.Proceedings of the European Conference on Computer Vision,pages 286-296,1994.
[104]刘宏,刘宏,李锦涛,et al.多方法融合来解决人脸检测中的光照补偿.系统仿真学报,13:486-489,2001.
[105]A.Shashua and T.Riklin-Raviv.The Quotient Image:Class-Based Re-Rendering and Recognition with Varying Illuminations.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 129-139,2001.
[106]Haitao Wang,Stan Z.Li,and Yangsheng Wang."Face recognition under various lighting conditions by self quotient image".The 6th International Conference on Automatic Face and Gesture Recognition,pages 819-824,May 2004.
[107]DJ Jobson,Z.Rahman,GA Woodell,N.L.R.Center,and VA Hampton.A multiscale retinex for bridging the gap between color images andthe human observation of scenes.Image Processing,IEEE Transactions on,6(7):965-976,1997.
[108]C.P.Chen and C.S.Chen.Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition.Proceedings of the Tenth IEEE International Conference on Computer Vision-Volume 2,pages 1089-1096,2005.
[109]W.Zhao and R.Chellappa.Robust face recognition using symmetric shape-from-shading.University of Maryland,CARTR-919,1999.
[110]L.Zheng.A New Model-based Lighting Normalization Algorithm and its Application in Face Recognition.PhD thesis,Master's thesis,National University of Singapore,2000.
[111]SG Shan,W.Gao,W.Wang,DB Zhao,and BC Yin.Enhanced Active Shape Models with Global Texture Constraints for Face Image Analysis.Fourteenth Internatioanal Symposium on Methodlogier For Intelligent Systems.N.Zhong et al.(Eds.):ISMIS,pages 593-507,2003.
[112]PW Hallinan.A low-dimensional representation of human faces for arbitrarylighting conditions.Computer Vision and Pattern Recognition,1994.Proceedings CVPR'94.,1994 IEEE Computer Society Conference on,pages 995-999,1994.
[113]R.Epstein,P.W.Hallinan,and A.L.Yuille.5 plus or minus 2 eigenimages suffice:an em- pirical investigation of low-dimensional lighting models.The Workshop on Physics-Based Modeling in Computer Vision,pages 108-116,1995.
[114]K.C.Lee,J.Ho,and D.Kriegman.Nine Points of Light:Acquiring Subspaces for Face Recognition under Variable Lighting.Urbana,51:61801.
[115]AL Yuille,D.Snow,R.Epstein,and PN Belhumeur.Determining Generative Models of Objects Under Varying Illumination:Shape and Albedo from Multiple Images Using SVD and Integrability.International Journal of Computer Vision,35(3):203-222,1999.
[116]A.Shashua.Illumination and view position in 3D visual recognition.Advances in Neural Information Processing Systems,4:68-74,1992.
[117]A.Shashua.On Photometric Issues in 3D Visual Recognition from a Single 2D Image.International Journal of Computer Vision,21(1):99-122,1997.
[118]R.Ishiyama and S.Sakamoto.Geodesic illumination basis:compensating for illumination variations in any pose for face recognition.Pattern Recognition,2002.Proceedings.16th International Conference on,4,2002.
[119]S.R.Marschner,D.P.Greenberg,and N.Y.Ithaca.Inverse Lighting for Photography.Presented at the IS(?)T/SID Fifth Color Imaging Conference,1997.
[120]P.N.Belhumeur and D.J.Kriegman.What is the set of images of an object under all possible lighting conditions.Proc.IEEE Conf on Comp.Vision and Patt.Recog,pages 270-277,1996.
[121]K.Nishino,P.N.Belhumeur,and S.K.Nayar.Using Eye Reflections for Face Recognition Under Varying Illumination.Proceedings of the Tenth IEEE International Conference on Computer Vision(ICCV'05)Volume I-Volume 01,pages 519-526,2005.
[122]G.Aggarwal and R.Chellappa.Face Recognition in the Presence of Multiple Illumination Sources.IEEE International Conference on Computer Vision,2005.
[123]R.Basri and D.W.Jacobs.Lambertian Reflectance and Linear Subspaces.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 218-233,2003.
[124]P.J.Phillips,P.J.Rauss,and S.Z.Der.FERET(Face Recognition Technology)Recognition Algorithm Development and Test Results.Army Research Laboratory technical report,ARLTR-995.http://www.frvt.org,1996.
[125]PJ Phillips,P.Grother,R.Micheals,DM Blackburn,E.Tabassi,M.Bone,and A.DARPA.Face recognition vendor test 2002.Analysis and Modeling of Faces and Gestures,2003.AMFG 2003.IEEE International Workshop on,2003.
[126]A.Pentland,B.Moghaddam,and T.Starner.View-based and modular eigenspaces for face recognition.Computer Vision and Pattern Recognition,1994.Proceedings CVPR '94.,1994IEEE Computer Society.Conference on,pages 84-91,1994.
[127]T.Thomas.Linear Object Classes and Image Synthesis From a Single Example Image.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 733-742,1997.
[128]T.Vetter.Synthesis of Novel Views from a Single Face Image.International Journal of Computer Vision,28(2):103-116,1998.
[129]D.Beymer and T.Poggio.Face recognition from one example view.Science,272(5250):1905-1909,1996.
[130]M.Bichsel.Automatic interpolation and recognition of face images by morphing.Proceedings of the International Conference on Automatic Face and Gesture Recognition,ICAFGR,96:128-135.
[131]T.Ezzat and T.Poggio.Facial analysis and synthesis using image-based models.Int.Conf.on Auto.Face and Gesture Recog,pages 116-121,1996.
[132]R.Gross,I.Matthews,and S.Baker.Eigen light-fields and face recognition across pose.Automatic Face and Gesture Recognition,2002.Proceedings.Fifth IEEE International Conference on,pages 1-7,2002.
[133]H.Murase and S.K.Nayar.Visual learning and recognition of 3-d objects from appearance.International Journal of Computer Vision,14(1):5-24,1995.
[134]H.Li,A.Lundmark,and R.Forchheimer.Image sequence coding at very low bit rates:a review.Image Processing,IEEE Transactions on,3(5):589-609,1994.
[135]R.Lengagne,J.P.Tarel,and O.Monga.From 2D Images to 3D Face Geometry.Proceedings of IEEE Second International Conference on Automatic Face and Gesture Recognition,pages 301-306,1996.
[136]J.Heinzmann and A.Zelinsky.3-D Facial Pose and Gaze Point Estimation using a Robust Real-Time Tracking Paradigm.Proc.of the Int.Conf.on Automatic Face and Gesture Recognition,pages 142-147,1998.
[137]T.Akimoto,Y.Suenaga,RS Wallace,and K.NTT.Automatic creation of 3D facial models.Computer Graphics and Applications,IEEE,13(5):16-22,1993.
[138]M.H.Yang,N.Abuja,and D.Kriegman.Face detection using mixtures of linear subspaces.Automatic Face and Gesture Recognition,2000.Proceedings.Fourth IEEE International Conference on,pages 70-76,2000.
[139]张翔宇and林志勇.从正面侧照片合成三维人脸.计算机应用,20(007):41-45,2000.
[140]Z.Liu,Z.Zhang,C.Jacobs,and M.Cohen.Rapid modeling of animated faces from video.Journal of Visualization and Computer Animation,12(4):227-240,2001.
[141]徐成华,王蕴红,and谭铁牛.三维人脸建模与应用.中国图象图形学报:A辑,9(008):893-903.2004.
[142]V.Bianz,T.Vetter,and G.Tubingen.A Morphable Model For The Synthesis Of 3D Faces.
[143]V.Blanz,S.Romdhani,and T.Vctter.Face identification across different poses and illuminations with a3D morphable model.Automatic Face and Gesture Recognition,2002.Proceedings.Fifth IEEE International Conference on,pages 192-197,2002.
[144]V.Blanz and T.Vetter.Face Recognition Based on Fitting a 3D Morphable Model.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 1063-1074,2003.
[145]B.Moghaddam and A.Pentland.An automatic system for model-based coding of faces.IEEE Data Compression Conference,pages 1-5,1995.
[146]D.Jiang,Y.Hu,S.Yan,L.Zhang,H.Zhang,and W.Gao.Efficient 3D reconstruction for face recognition.Pattern Recognition,38(6):787-798,2005.
[147]R.Zhang,P.S.Tsai,J.E.Cryer,and M.Shah.Shape from Shading:A Survey.1999.
[148]T.Ojala,M.Pietikainen,and M.Maenpaa."Multiresolution gray-scale and rotation invariant texture classification width local binary patterns".IEEE Transactions on Pattern Analysis and Machine Intelligence,24:971-987,2002.
[149]A.Hadid.Face Description with Local Binary Patterns:Application to Face Recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence,28(12):2037-2041,2006.
[150]S.Z.Li,R.F.Chu,S.C.Liao,and L.Zhang.Illumination Invariant Face Recognition Using Near-lnfrared Images.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 627-639,2007.
[151]P.Grother and E.Tabassi.Performance of Biometric Quality Measures.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,pages 531-543,2007.
[152]E.Tabassi,C.Wilson,and C.Watson.Fingerprint Image Quality.NIST research report NISTIR7151(August,2004).
[153]Y.Freund and R.E.Schapire.A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1): 119-139, 1997.
[154] J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Statist, 28(2):337-407,2000.
[155] S.K. Zhou, B. Georgescu, X.S. Zhou, and D. Comaniciu. Image Based Regression Using Boosting Method. Proc. Int'l Conf. Computer Vision, 1:541-548, 2005.
[156] E. Tabassi and CL Wilson. A Novel Approach to Fingerprint Image Quality. Image Processing, 2005. ICIP 2005. IEEE International Conference on, 2, 2005.
[157] N. Duffy and D. Helmbold. Boosting Methods for Regression. Machine Learning, 47(2): 153-200,2002.
[158] Stan Z. Li and and His Face Team. "AuthenMetric F1: A Highly Accurate and Fast Face Recognition System". ICCV2005 - Demos, October 15-21 2005.
[159] S. Z. Li, R. F. Chu, M. Ao, L. Zhang, and R. He. "Highly accurate and fast face recognition using near infrared images". In Proceedings of IAPR International Conference on Biometric (ICB-2006), pages 151-158, Hong Kong, January 2006.
[160] A. Samal and P. A.Iyengar. "Automatic recognition and analysis of human faces and facial expressions". Pattern Recognition, 25:66-77, 1992.
[161] D. Valentin, H. Abdi, A. J. O'Toole, and G. W. Cottrell. "Connectionist models of face processing: A survey". Pattern Recognition, 27(9): 1209-1230, 1994.
[162] R. Chellappa, C. Wilson, and S. Sirohey. "Human and machine recognition of faces: A survey". Proceedings of the IEEE, 83:705-740, 1995.
[163] X. Zhu, Y. Liu, X. Ming, and Q. Cui. "A quality evaluation method of iris images sequence based on wavelet coefficients in 'region of interest' ". Proc. of the 4th Int'l Conf. on Computer and Information Technology, pages 24-27, September 2004.
[164] Y. Chen, S. C. Dass, and A. K. Jain. "Localized iris image quality using 2-d wavelets". Proc.of International Conference on Biometrics (ICB), pages 373-381, January 2006.
[165] Frank Weber. "Some quality measures for face images and their relationship to recognition performance". Cognitec systems gmbh, NIST Biometric Quality Workshop, 2006.
[166] Martin Werner and Michael Brauckmann. "Quality values for face recognition". Viisage inc, NIST Biometric Quality Workshop, 2006. http://www.itl.nist.gov/iad/894.03/quality/workshop/.
[167]Y.Liu,K.Schmidt,J.Cohn,and R.L.Weaver."Facial asymmetry quantification for expression invariant human identification".International Conference on Automatic Face and Gesture Recognition,2002.
[168]T.Ko and R.Krishnan.Monitoring and reporting of fingerprint image quality and match accuracy for a large user application.Applied Imagery Pattern Recognition Workshop,2004.Proceedings.33rd,pages 159-164,2004.
[169]T.Ko and R.Krishnan.A Look Beyond Data Exchange to Fingerprint Scanner Interoperability.Biometric Consortium Conference,Washington DC,September,2003.
[170]Yan Zhang and Jufu Feng."Eliminating variation of face images using face symmetry".AVBPA,pages 523-530,2003.
[171]Guangcheng Zhang,Xiangsheng Huang,Stan Z.Li,Yangsheng Wang,and Xihong Wu."Boosting local binary pattern(Ibp)-based face recognition".Proc.Advances in Biometric Person Authentication:5th Chinese Conference on Biometric Recognition,pages 179-186,December 2004.
[172]A.Hicklin and R.Khanna.The Role of Data Quality in Biometric Systems.February 2006.http://www.mitretek.org/Role_of_Data_Quality_Final.pdf.
[173]Peng Yang,Shiguang Shah,Wen Gao,Start Z.Li,and Dong Zhang."Face recognition using ada-boosted gabor features".The 6th International Conference on Automatic Face and Gesture Recognition,pages 356-361,May 2004.
[174]C.Liu and H.Wechsler.Gabor feature based classification using the enhanced fisher lineardiscriminant model for face recognition.Image Processing,IEEE Transactions on,11(4):467-476,2002.
[175]T.Ojala,M.Pietikainen,and D.Harwood."A comparative study of texture measures with classification based on feature distributions".Pattern Recognition,29(1):51-59,January 1996.
[176]J.Sadr,S.Mukherjee,K.Thoresz,and P.Sinha."The fidelity of local ordinal encoding".In Proceedings of the Fifteenth Annual Conference on Neural Information Processing Systems (NIPS 2001),pages 1279-1286,December 2001.
[177]P.Sinha."Qualitative representations for recognition".In Proceedings of the Second International Workshop on Biologically Motivated Computer Vision,pages 249-262,November 2002.
[178]K.J.Thoresz.On qualitative representations for recognition.Master's thesis,MIT,July 2002.
[179] Shengcai Liao, Zhen Lei, Xiangxin Zhu, Zhenan Sun, Stan Z. Li, and Tieniu Tan. "Face recognition using ordinal features". In Proceedings of IAPR International Conference on Biometrics, (ICB-2006), pages 40-46, Jan 2006.
[180] Stan Z. Li, RuFeng Chu, Meng Ao, Lun Zhang, and Ran He. "Highly accurate and fast face recognition using near infrared images". In Proceedings of IAPR International Conference on Biometrics, (ICB-2006), pages 151-158, Jan 2006.
[181] Richard Youmaran and Andy Adler. "Measuring biometric sample quality in terms of biometric information". Biometrics Consortium Conference, Sep 2006.
[182] Stan Z. Li, RuFeng Chu, ShengCai Liao, and Lun Zhang. "Illumination invariant face recognition using near-infrared images". 26(Special issu e on Biometrics: Progress and Directions):627-639, April 2007.
[183] Stan Z. Li, Lun Zhang, ShengCai Liao, XiangXin Zhu, RuFeng Chu, Meng Ao, and Ran He. "A near-infrared image based face recognition system". The 7th International Conference on Automatic Face and Gesture Recognition, pages 455- 460, April 2006.
[184] Wen Gao, Bo Cao, Shiguang Shan, Delong Zhou, Xiaohua Zhang, and Debin Zhao. "The cas-peal large-scale chinese face database and baseline evaluations". Technical Report JDL-TR-04-FR-001, Beijing: Joint Research and Development Laboratory the Chinese Academy of Sciences, May 2004.
[185] ISO/IEC JTC 1/SC 37 N 506. Biometric Data Interchange Formats -Part 5: Face Image Data. March 22, 2004.
[186] ISO/IEC JTC 1/SC37 N 1128. Quality Rapporteur Group Report, 2005.
[187] QB Sun, WM Huang, and JK Wu. Face detection based on color and local symmetry information. Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on, pages 130-135,1998.
[188] Sinjini Mitra and Yanxi Liu. Local facial asymmetry for expression classification. In Proceedings of the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'04), volume 2, pages 889 - 894, June 2004.
[189] A. Nikolaidis and I. Pitas. Facial feature extraction and pose determination. Pattern Recognition, 33(11): 1783-1791, 2000.
[190] K. Hattori, S. Matsumori, and Y. Sato. Estimating pose of human face based on symmetry plane using rangeand intensity images. Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on,2,1998.
[191]E.J.Huang,ZH Zhou,H.J.Zhang,and T.Chen.Pose invariant face recognition.Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition,pages 245-250,2000.
[192]L.Yi and Z.Rong-chun.Analysis and evaluation of several typical SFS algorithms.Journal of Image and Graphics,6(10):953-961,2001.