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Artificial Intelligence Review

, Volume 44, Issue 3, pp 393–441 | Cite as

3-D face recognition: features, databases, algorithms and challenges

  • Hemprasad Patil
  • Ashwin Kothari
  • Kishor Bhurchandi
Article

Abstract

Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area.

Keywords

Face recognition 3-D faces Feature extraction  3-D Face databases Biometrics Face matching Classifiers 

References

  1. Abate AF, Nappi M, Riccio D, Sabatino G (2006) 3D face recognition using normal sphere and general Fourier descriptor. In: 18th international conference on pattern recognition, pp 1183–1186. doi: 10.1109/ICPR.2006.25
  2. Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recogn Lett 28:1885–1906. doi: 10.1016/j.patrec.2006.12.018 CrossRefGoogle Scholar
  3. Achermann B, Bunke H (2000) Classifying range images of human faces with Hausdorff distance. In: 15th international conference on pattern recognition, pp 809–813. doi: 10.1109/ICPR.2000.906199
  4. Al-Osaimi F, Bennamoun M, Mian A (2009) An expression deformation approach to non-rigid 3D face recognition. Int J Comput Vis 81:302–316. doi: 10.1007/s11263-008-0174-0 CrossRefGoogle Scholar
  5. Al-Osaimi FR, Bennamoun M, Mian A (2012) Spatially optimized data-level fusion of texture and shape for face recognition. IEEE Trans Image Process 21:859–872. doi: 10.1109/TIP.2011.2165218 MathSciNetCrossRefGoogle Scholar
  6. Alyuz N, Gokberk B, Akarun L (2010) Regional registration for expression resistant 3-D face recognition. IEEE Trans Inf Forensics Secur 5:425–440. doi: 10.1109/TIFS.2010.2054081 CrossRefGoogle Scholar
  7. Alyuz N, Gokberk B, Akarun L (2013) 3-D face recognition under occlusion using masked projection. IEEE Trans Inf Forensics Secur 8:789–802. doi: 10.1109/TIFS.2013.2256130 CrossRefGoogle Scholar
  8. Alyuz N, Gokberk B, Dibeklioglu H, Akarun L (2008) Component-based registration with curvature descriptors for expression insensitive 3D face recognition. In: 8th IEEE international conference on automatic face and gesture recognition, pp 1–6. doi: 10.1109/AFGR.2008.4813359
  9. Alyuz N, Gokberk B, Spreeuwers L, Veldhuis R, Akarun L (2012) Robust 3D face recognition in the presence of realistic occlusions. In: 5th IAPR international conference on biometrics, pp 111–118. doi: 10.1109/ICB.2012.6199767
  10. Amberg B, Knothe R, Vetter T (2008) Expression invariant 3D face recognition with a Morphable model. In: 8th IEEE international conference on automatic face and gesture recognition, pp 1–6. doi: 10.1109/AFGR.2008.4813376
  11. Amor BB, Ardabilian M, Chen L (2013) 3D face modeling. In: 3D face modeling, analysis and recognition. Wiley, Singapore, pp 1–37. doi: 10.1002/9781118592656.ch1
  12. Ballihi L, Ben Amor B, Daoudi M, Srivastava A, Aboutajdine D (2012) Boosting 3-D-geometric features for efficient face recognition and gender classification. IEEE Trans Inf Forensics Secur 7:1766–1779. doi: 10.1109/TIFS.2012.2209876 CrossRefGoogle Scholar
  13. Bao-Cai Y, Yan-Feng S, Cheng-Zhang W, Yun G (2009) BJUT-3D large scale 3D face database and information processing. J Comput Res Dev 46:1009–1018Google Scholar
  14. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720. doi: 10.1109/34.598228 CrossRefGoogle Scholar
  15. Bellil W, Brahim H, Ben Amar C (2014) Gappy wavelet neural network for 3D occluded faces: detection and recognition. Multimed Tools Appl:1–16 doi: 10.1007/s11042-014-2294-6
  16. Bellon O, Silva L, Queirolo C, Drovetto S, Pamplona M (2006) 3D face image registration for face matching guided by the surface interpenetration measure. In: IEEE international conference on image processing, pp 2661–2664. doi: 10.1109/ICIP.2006.313057
  17. Ben Amor B, Ardabilian M, Liming C (2008) Toward a region-based 3D face recognition approach. In: IEEE international conference on multimedia and expo, pp 101–104. doi: 10.1109/ICME.2008.4607381
  18. Berretti S, Del Bimbo A, Pala P (2010) 3D face recognition using isogeodesic stripes. IEEE Trans Pattern Anal Mach Intell 32:2162–2177. doi: 10.1109/TPAMI.2010.43 CrossRefGoogle Scholar
  19. Berretti S, Del Bimbo A, Pala P (2013) Sparse matching of salient facial curves for recognition of 3-D faces with missing parts. IEEE Trans Inf Forensics Secur 8:374–389. doi: 10.1109/TIFS.2012.2235833 CrossRefGoogle Scholar
  20. Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256. doi: 10.1109/34.121791 CrossRefGoogle Scholar
  21. Beumier C, Acheroy M (2000) Automatic 3D face authentication. Image Vis Comput 18:315–321. doi: 10.1016/S0262-8856(99)00052-9 CrossRefGoogle Scholar
  22. Blackburn DM, Bone M, Phillips PJ (2001) Face recognition vendor test 2000: evaluation report. DTIC Document, http://www.face-rec.org/vendors/FRVT_2002_Evaluation_Report.pdf. Accessed Dec 20, 2013
  23. Boehnen C, Peters T, Flynn P (2009) 3D Signatures for fast 3D face recognition. In: Tistarelli M, Nixon M (eds) Advances in biometrics, vol 5558. Lecture notes in computer science. Springer, Berlin, pp 12–21. doi: 10.1007/978-3-642-01793-3_2
  24. Bowyer KW, Chang K, Flynn P (2006) A survey of approaches and challenges in 3D and multi-modal 3D \(+\) 2D face recognition. Comput Vis Image Underst 101:1–15. doi: 10.1016/j.cviu.2005.05.005 CrossRefGoogle Scholar
  25. Breiman L (2001) Random forests. Mach Learn 45:5–32. doi: 10.1023/A:1010933404324 zbMATHCrossRefGoogle Scholar
  26. Breitenstein MD, Kuettel D, Weise T, Van Gool L, Pfister H (2008) Real-time face pose estimation from single range images. In: IEEE conference on computer vision and pattern recognition, pp 1–8. doi: 10.1109/CVPR.2008.4587807
  27. Cai L, Da F (2012) Estimating inter-personal deformation with multi-scale modelling between expression for three-dimensional face recognition. IET Comput Vis 6:468–479. doi: 10.1049/iet-cvi.2011.0105 CrossRefGoogle Scholar
  28. Chang KI, Bowyer W, Flynn PJ (2006) Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans Pattern Anal Mach Intell 28:1695–1700. doi: 10.1109/TPAMI.2006.210 CrossRefGoogle Scholar
  29. Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83:705–741. doi: 10.1109/5.381842 CrossRefGoogle Scholar
  30. Chenghua X, Yunhong W, Tieniu T, Long Q (2004) Automatic 3D face recognition combining global geometric features with local shape variation information. In: Sixth IEEE international conference on automatic face and gesture recognition, pp 308–313. doi: 10.1109/AFGR.2004.1301549
  31. Chew WJ, Seng KP, Ang L-M (2009) Nose tip detection on a three-dimensional face range image invariant to head pose. In: Proceedings of the international multiconference of engineers and computer scientists, pp 858–862Google Scholar
  32. Colbry D, Stockman G, Jain A (2005) Detection of anchor points for 3D face Veri.cation. In: IEEE computer society conference on computer vision and pattern recognition—workshops, pp 118–118. doi: 10.1109/CVPR.2005.441
  33. Colineau J, D’Hose J, Amor B, Ardabilian M, Chen L, Dorizzi B (2008) 3D face recognition evaluation on expressive faces using the IV2 database. In: Blanc-Talon J, Bourennane S, Philips W, Popescu D, Scheunders P (eds) Advanced concepts for intelligent vision systems, vol 5259. Lecture notes in computer science. Springer, Berlin, pp 1050–1061. doi: 10.1007/978-3-540-88458-3_95
  34. Colombo A, Cusano C, Schettini R (2006) 3D face detection using curvature analysis. Pattern Recogn 39:444–455zbMATHCrossRefGoogle Scholar
  35. Colombo A, Cusano C, Schettini R (2008) Recognizing faces In 3D images even in presence of occlusions. In: 2nd IEEE international conference on biometrics: theory, applications and systems, pp 1–6. doi: 10.1109/BTAS.2008.4699345
  36. Colombo A, Cusano C, Schettini R (2011) UMB-DB: a database of partially occluded 3D faces. In: IEEE international conference on computer vision workshops, pp 2113–2119. doi: 10.1109/ICCVW.2011.6130509
  37. Conde C, Rodríguez-Aragón L, Cabello E (2006a) Automatic 3D face feature points extraction with spin images. In: Campilho A, Kamel M (eds) Image analysis and recognition, vol 4142. Lecture notes in computer science. Springer, Berlin, pp 317–328. doi: 10.1007/11867661_29
  38. Conde C, Serrano A (2005) 3D Facial normalization with spin images and influence of range data calculation over face verification. In: IEEE Computer Society conference on computer vision and pattern recognition, pp 115–115. doi: 10.1109/CVPR.2005.379
  39. Conde C, Serrano A, Cabello E (2006b) Multimodal 2D, 2.5D & 3D face verification. In: IEEE international conference on image processing, pp 2061–2064. doi: 10.1109/ICIP.2006.312863
  40. Cook J, Chandran V, Sridharan S, Fookes C (2004) Face recognition from 3D data using iterative closest point algorithm and gaussian mixture models. In: 2nd international symposium on 3D data processing, visualization and transmission, pp 502–509Google Scholar
  41. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297. doi: 10.1023/A:1022627411411 zbMATHGoogle Scholar
  42. Cover T, Hart P (2006) Nearest neighbor pattern classification. IEEE Trans Inf Theor 13:21–27. doi: 10.1109/tit.1967.1053964 CrossRefGoogle Scholar
  43. Daoudi M, Srivastava A, Veltkamp R (2013) 3D face modeling, analysis and recognition. Wiley, LondonCrossRefGoogle Scholar
  44. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Opt Soc Am J A Opt Image Sci 2:1160–1169CrossRefGoogle Scholar
  45. D’Hose J, Colineau J, Bichon C, Dorizzi B (2007) Precise localization of landmarks on 3D faces using gabor wavelets. In: First IEEE international conference on biometrics: theory, applications, and systems, pp 1–6. doi: 10.1109/BTAS.2007.4401927
  46. Di H, Ardabilian M, Yunhong W, Liming C (2012) 3-D face recognition using eLBP-based facial description and local feature hybrid matching. IEEE Trans Inf Forensics Secur 7:1551–1565. doi: 10.1109/TIFS.2012.2206807 CrossRefGoogle Scholar
  47. Dibeklioglu H, Salah AA, Akarun L (2008) 3D facial landmarking under expression, pose, and occlusion variations. In: 2nd IEEE international conference on biometrics: theory, applications and systems, pp 1–6. doi: 10.1109/BTAS.2008.4699324
  48. Di H, Guangpeng Z, Ardabilian M, Yunhong W, Liming C (2010) 3D face recognition using distinctiveness enhanced facial representations and local feature hybrid matching. In: Fourth IEEE international conference on biometrics: theory applications and systems, pp 1–7. doi: 10.1109/BTAS.2010.5634497
  49. Drira H, Ben Amor B, Srivastava A, Daoudi M, Slama R (2013) 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans Pattern Anal Mach Intell 35:2270–2283. doi: 10.1109/TPAMI.2013.48 CrossRefGoogle Scholar
  50. Elaiwat S, Bennamoun M, Boussaid F, El-Sallam A (2014) 3-D face recognition using curvelet local features. IEEE Signal Process Lett 21:172–175. doi: 10.1109/LSP.2013.2295119 CrossRefGoogle Scholar
  51. Faltemier TC, Bowyer KW, Flynn PJ (2007) Using a multi-instance enrollment representation to improve 3D face recognition. In: First IEEE international conference on biometrics: theory, applications, and systems, pp 1–6. doi: 10.1109/BTAS.2007.4401928
  52. Faltemier TC, Bowyer KW, Flynn PJ (2008) A region ensemble for 3-D face recognition. IEEE Trans Inf Forensics Secur 3:62–73. doi: 10.1109/TIFS.2007.916287 CrossRefGoogle Scholar
  53. Farkas L (1994) Anthropometry of the head and face, 2nd edn. Raven Press, New YorkGoogle Scholar
  54. Fels M, Olver P (1998) Moving coframes: I. A practical algorithm. Acta Appl Math 51:161–213. doi: 10.1023/A:1005878210297 MathSciNetCrossRefGoogle Scholar
  55. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. ACM Commun 24:381–395. doi: 10.1145/358669.358692 MathSciNetCrossRefGoogle Scholar
  56. Freund Y, Schapire R, Abe N (1999) A short introduction to boosting. J Jpn Soc For Artif Intell 14:1612Google Scholar
  57. Gang P, Yijun W, Wu Z (2003) Investigating profile extracted from range data for 3D face recognition. In: IEEE international conference on systems, man and cybernetics, pp 1396–1399. doi: 10.1109/ICSMC.2003.1244607
  58. Gokberk B, Dutagaci H, Ulas A, Akarun L, Sankur B (2008) Representation plurality and fusion for 3-D face recognition. IEEE Trans Syst Man Cybern Part B Cybern 38:155–173. doi: 10.1109/TSMCB.2007.908865 CrossRefGoogle Scholar
  59. Gökberk B, Salah A, Alyüz N, Akarun L (2009) 3D face recognition: technology and applications. In: Tistarelli M, Li S, Chellappa R (eds) Handbook of remote biometrics. Advances in pattern recognition. Springer, London, pp 217–246. doi: 10.1007/978-1-84882-385-3_9
  60. Gordon GG (1992) Face recognition based on depth and curvature features. In: IEEE computer society conference on computer vision and pattern recognition, pp 808–810. doi: 10.1109/CVPR.1992.223253
  61. Gupta S, Markey M, Bovik A (2010b) Anthropometric 3D face recognition. Int J Comput Vis 90:331–349. doi: 10.1007/s11263-010-0360-8 CrossRefGoogle Scholar
  62. Gupta S, Castleman KR, Markey MK, Bovik AC (2010a) Texas 3D face recognition database. In: IEEE southwest symposium on image analysis and interpretation, pp 97–100. doi: 10.1109/SSIAI.2010.5483908
  63. Gupta S, Markey MK, Aggarwal JK, Bovik AC (2007) Three dimensional face recognition based on geodesic and Euclidean distances. In: Proceedings of SPIE 6499, vision geometry XV, pp 64990D–64990D-64911. doi: 10.1117/12.704535
  64. Haasbroek ND (1968) Gemma Frisius. Rijkscommissie voor Geodesie, Delft, W. D. Meinema, Netherlands, Tycho Brahe and Snellius and their triangulationsGoogle Scholar
  65. Heseltine T, Pears N, Austin J (2008) Three-dimensional face recognition using combinations of surface feature map subspace components. Image Vis Comput 26:382–396. doi: 10.1016/j.imavis.2006.12.008 CrossRefGoogle Scholar
  66. Heseltine T, Pears N, Austin J (2004) Three-dimensional face recognition: an eigensurface approach. In: International conference on image processing, pp 1421–1424. doi: 10.1109/ICIP.2004.1419769
  67. Hesher C, Srivastava A, Erlebacher G (2003) A novel technique for face recognition using range imaging. In: Seventh international symposium on signal processing and its applications, pp 201–204. doi: 10.1109/ISSPA.2003.1224850
  68. Huang D, Ouji K, Ardabilian M, Wang Y, Chen L (2011) 3D Face recognition based on local shape patterns and sparse representation classifier. In: Lee K-T, Tsai W-H, Liao H-Y, Chen T, Hsieh J-W, Tseng C-C (eds) Advances in multimedia modeling, vol 6523. Lecture notes in computer science. Springer, Berlin, pp 206–216. doi: 10.1007/978-3-642-17832-0_20
  69. Huang Y, Wang Y, Tan T (2006) Combining statistics of geometrical and correlative features for 3D face recognition. In: British machine vision conference, BMVA Press, pp 879–888. doi: 10.5244/C.20.90
  70. Huynh T, Min R, Dugelay J-L (2013) An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In: Park J-I, Kim J (eds) Computer vision—ACCV 2012 workshops, vol 7728. Lecture notes in computer science. Springer, Berlin, pp 133–145. doi: 10.1007/978-3-642-37410-4_12
  71. Hyoungchul S, Kwanghoon S (2006) 3D face recognition with geometrically localized surface shape indexes. In: 9th international conference on control, automation, robotics and vision, pp 1–6. doi: 10.1109/ICARCV.2006.345192
  72. Jahanbin S, Bovik AC, Hyohoon C (2008) Automated facial feature detection from portrait and range images. In: IEEE southwest symposium on image analysis and interpretation, pp 25–28. doi: 10.1109/SSIAI.2008.4512276
  73. Jahanbin S, Hyohoon C, Bovik AC (2007) Castleman KR three dimensional face recognition using wavelet decomposition of range images. In: IEEE international conference on image processing, pp 145–148. doi: 10.1109/ICIP.2007.4378912
  74. Jain AK, Flynn PJ, Ross AA (2008) Handbook of biometrics, 2nd edn. Springer, USA. doi: 10.1007/978-0-387-71041-9
  75. Jaiswal S, Bhadauria S, Jadon R, Divakar T (2011) Brief description of image based 3D face recognition methods. 3D Res 1:1–14. doi: 10.1007/3DRes.04(2010)02
  76. Je C, Lee KH, Lee SW (2013) Multi-projector color structured-light vision. Sig Process Image Commun 28:1046–1058. doi: 10.1016/j.image.2013.05.005 CrossRefGoogle Scholar
  77. Johnson AE, Hebert M (1998) Surface matching for object recognition in complex three-dimensional scenes. Image Vis Comput 16:635–651. doi: 10.1016/S0262-8856(98)00074-2 CrossRefGoogle Scholar
  78. Johnson AE, Hebert M (1999) Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans Pattern Anal Mach Intell 21:433–449. doi: 10.1109/34.765655 CrossRefGoogle Scholar
  79. Kakadiaris IA, Passalis G, Toderici G, Murtuza MN, Yunliang L, Karampatziakis N, Theoharis T (2007) Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans Pattern Anal Mach Intell 29:640–649. doi: 10.1109/TPAMI.2007.1017 CrossRefGoogle Scholar
  80. Kin-Chung W, Wei-Yang L, Yu Hen H, Boston N, Xueqin Z (2007) Optimal linear combination of facial regions for improving identification performance. IEEE Trans Syst Man Cybern Part B Cybern 37:1138–1148. doi: 10.1109/TSMCB.2007.895325 CrossRefGoogle Scholar
  81. Kinect (2013) Kinect for Windows. http://www.microsoft.com/en-us/kinectforwindows/. Accessed Dec 20, 2013
  82. Kisačanin B, Nikolić Z (2010) Algorithmic and software techniques for embedded vision on programmable processors. Sig Process Image Commun 25:352–362. doi: 10.1016/j.image.2010.02.003 CrossRefGoogle Scholar
  83. Koch R, Pears N, Liu Y (2012) 3D imaging, analysis and applications. Springer, London. doi: 10.1007/978-1-4471-4063-4
  84. Konica (2013) Konica minolta color, light and shape measuring instruments. http://sensing.konicaminolta.us/applications/3d-scanners/. Accessed Dec 20, 2013
  85. Koschan A, Pollefeys M, Abidi M (2007) 3D imaging for safety and security (computational imaging and vision). Springer, New York. doi: 10.1007/978-1-4020-6182-0 CrossRefGoogle Scholar
  86. Koudelka ML, Koch MW, Russ TD (2005) A Prescreener for 3D face recognition using radial symmetry and the hausdorff fraction. In: IEEE computer society conference on computer vision and pattern recognition, pp 168–168. doi: 10.1109/CVPR.2005.566
  87. Lei Y, Bennamoun M, El-Sallam AA (2013) An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recogn 46:24–37. doi: 10.1016/j.patcog.2012.06.023 CrossRefGoogle Scholar
  88. Lei Y, Bennamoun M, Hayat M, Guo Y (2014) An efficient 3D face recognition approach using local geometrical signatures. Pattern Recogn 47:509–524. doi: 10.1016/j.patcog.2013.07.018 CrossRefGoogle Scholar
  89. Li BYL, Mian AS, Wanquan L, Krishna A (2013) Using Kinect for face recognition under varying poses, expressions, illumination and disguise. In: IEEE workshop on applications of computer vision, pp 186–192. doi: 10.1109/WACV.2013.6475017
  90. Li SZ, Jain AK (2011) Handbook of face recognition, 2nd edn. Springer, London. doi: 10.1007/978-0-85729-932-1
  91. Li H, Huang D, Morvan J-M, Chen L, Wang Y (2014) Expression-robust 3D face recognition via weighted sparse representation of multi-scale and multi-component local normal patterns. Neurocomputing 133:179–193. doi: 10.1016/j.neucom.2013.11.018 CrossRefGoogle Scholar
  92. Li X, Da F (2012) Efficient 3D face recognition handling facial expression and hair occlusion. Image Vis Comput 30:668–679. doi: 10.1016/j.imavis.2012.07.011 CrossRefGoogle Scholar
  93. Lijun Y, Xiaozhou W, Yi S, Jun W, Rosato MJ (2006) A 3D facial expression database for facial behavior research. In: 7th international conference on automatic face and gesture recognition, pp 211–216. doi: 10.1109/FGR.2006.6
  94. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. doi: 10.1023/B:VISI.0000029664.99615.94 CrossRefGoogle Scholar
  95. Määttä J, Hadid A, Pietikäinen M (2012) Face spoofing detection from single images using texture and local shape analysis. IET Biometrics 1:3–10. doi: 10.1049/iet-bmt.2011.0009 CrossRefGoogle Scholar
  96. Maes C, Fabry T, Keustermans J, Smeets D, Suetens P, Vandermeulen D (2010) Feature detection on 3D face surfaces for pose normalisation and recognition. In: Fourth IEEE international conference on biometrics: theory applications and systems, pp 1–6. doi: 10.1109/BTAS.2010.5634543
  97. Malassiotis S, Strintzis MG (2005) Robust real-time 3D head pose estimation from range data. Pattern Recogn 38:1153–1165. doi: 10.1016/j.patcog.2004.11.020 CrossRefGoogle Scholar
  98. Metaxas DN, Kakadiaris IA (2002) Elastically adaptive deformable models. IEEE Trans Pattern Anal Mach Intell 24:1310–1321. doi: 10.1109/TPAMI.2002.1039203 CrossRefGoogle Scholar
  99. Mian A (2011) Robust realtime feature detection in raw 3D face images. In: IEEE workshop onapplications of computer vision, pp 220–226. doi: 10.1109/WACV.2011.5711506
  100. Ming Y (2015) Robust regional bounding spherical descriptor for 3D face recognition and emotion analysis. Image Vis Comput 35:14–22. doi: 10.1016/j.imavis.2014.12.003 CrossRefGoogle Scholar
  101. Mohammadzade H, Hatzinakos D (2013) Iterative closest normal point for 3D face recognition. IEEE Trans Pattern Anal Mach Intell 35:381–397. doi: 10.1109/TPAMI.2012.107 CrossRefGoogle Scholar
  102. Moorthy AK, Mittal A, Jahanbin S, Grauman K, Bovik AC (2010) 3D facial similarity: automatic assessment versus perceptual judgments. In: Fourth IEEE international conference on biometrics: theory applications and systems, pp 1–7. doi: 10.1109/BTAS.2010.5634494
  103. Moreno A, Sanchez A (2004) GavabDB: a 3D face database. In: 2nd COST workshop on biometrics on the internet: fundamentals, advances and applications, pp 77–82Google Scholar
  104. Mousavi MH, Faez K, Asghari (2008) A three dimensional face recognition using SVM classifier. In: Seventh IEEE/ACIS international conference on computer and information science, pp 208–213. doi: 10.1109/ICIS.2008.77
  105. Mpiperis I, Malassiotis S, Strintzis MG (2008) Bilinear models for 3-D face and facial expression recognition. IEEE Trans Inf Forensics Secur 3:498–511. doi: 10.1109/TIFS.2008.924598 CrossRefGoogle Scholar
  106. Nair P, Cavallaro A (2009) 3-D face detection, landmark localization, and registration using a point distribution model. IEEE Trans Multimed 11:611–623. doi: 10.1109/TMM.2009.2017629 CrossRefGoogle Scholar
  107. Ocegueda O, Tianhong F, Shah SK, Kakadiaris IA (2013) 3D face discriminant analysis using Gauss–Markov posterior marginals. IEEE Trans Pattern Anal Mach Intell 35:728–739. doi: 10.1109/TPAMI.2012.126 CrossRefGoogle Scholar
  108. Ocegueda O, Shah SK, Kakadiaris IA (2011) Which parts of the face give out your identity? In: IEEE conference on computer vision and pattern recognition, pp 641–648. doi: 10.1109/CVPR.2011.5995613
  109. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987. doi: 10.1109/TPAMI.2002.1017623 CrossRefGoogle Scholar
  110. Passalis G, Perakis P, Theoharis T, Kakadiaris IA (2011) Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Trans Pattern Anal Mach Intell 33:1938–1951. doi: 10.1109/TPAMI.2011.49 CrossRefGoogle Scholar
  111. Peijiang L, Yunhong W, Di H, Zhaoxiang Z, Liming C (2013) Learning the spherical harmonic features for 3-D face recognition. IEEE Trans Image Process 22:914–925. doi: 10.1109/TIP.2012.2222897 MathSciNetCrossRefGoogle Scholar
  112. Peng H, Fulmi L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238. doi: 10.1109/TPAMI.2005.159 CrossRefGoogle Scholar
  113. Peng X, Bennamoun M, Mian AS (2011) A training-free nose tip detection method from face range images. Pattern Recogn 44:544–558. doi: 10.1016/j.patcog.2010.09.015 zbMATHCrossRefGoogle Scholar
  114. Perakis P, Passalis G, Theoharis T, Toderici G, Kakadiaris IA (2009) Partial matching of interpose 3D facial data for face recognition. In: IEEE 3rd international conference on biometrics: theory, applications, and systems, pp 1–8. doi: 10.1109/BTAS.2009.5339019
  115. Petrovska-Delacretaz D et al. (2008) The IV2 multimodal biometric database (including Iris, 2D, 3D, stereoscopic, and talking face data), and the IV2-2007 evaluation campaign. In: 2nd IEEE international conference on biometrics: theory, applications and systems, pp 1–7. doi: 10.1109/BTAS.2008.4699323
  116. Phillips PJ et al. (2005) Overview of the face recognition grand challenge. In: IEEE computer society conference on computer vision and pattern recognition, vol 941, pp 947–954. doi: 10.1109/CVPR.2005.268
  117. Phillips PJ, Scruggs WT, O’Toole AJ, Flynn PJ, Bowyer KW, Schott CL, Sharpe M (2010) FRVT 2006 and ICE 2006 large-scale experimental results. IEEE Trans Pattern Anal Mach Intell 32:831–846. doi: 10.1109/TPAMI.2009.59 CrossRefGoogle Scholar
  118. Phillips P, Grother P, Micheals R, Blackburn D, Tabassi E, Bone J (2003) FRVT 2002: evaluation report. http://www.frvt.org/FRVT2002. Accessed Dec 20, 2013
  119. Queirolo CC, Silva L, Bellon ORP, Segundo MP (2008) 3D face recognition using the surface interpenetration measure: a comparative evaluation on the FRGC database. In: 19th international conference on pattern recognition, pp 1–5. doi: 10.1109/ICPR.2008.4761696
  120. Queirolo CC, Silva L, Bellon ORP, Pamplona Segundo M (2010) 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Trans Pattern Anal Mach Intell 32:206–219. doi: 10.1109/TPAMI.2009.14 CrossRefGoogle Scholar
  121. Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23–69. doi: 10.1023/A:1025667309714 zbMATHCrossRefGoogle Scholar
  122. Romero M, Pears N (2009) Point-pair descriptors for 3D facial landmark localisation. In: IEEE 3rd international conference on biometrics: theory, applications, and systems, pp 1–6. doi: 10.1109/BTAS.2009.5339009
  123. Ross AA, Anil K. Jain, and Karthik Nandakumar (2006) Levels of fusion in biometrics. In: Handbook of multibiometrics, vol 6. international series on biometrics. Springer, USA, pp 59–90. doi: 10.1007/0-387-33123-9_3
  124. Russ TD, Koch MW, Little CQ (2005) A 2D range hausdorff approach for 3D face recognition. In: IEEE computer society conference on computer vision and pattern recognition—workshops, pp 169–169. doi: 10.1109/CVPR.2005.561
  125. Russ T, Boehnen C, Peters T (2006) 3D face recognition using 3D alignment for PCA. In: IEEE computer society conference on computer vision and pattern recognition, pp 1391–1398. doi: 10.1109/CVPR.2006.13
  126. Sala Llonch R, Kokiopoulou E, Tošić I, Frossard P (2010) 3D face recognition with sparse spherical representations. Pattern Recogn 43:824–834 doi: 10.1016/j.patcog.2009.07.005
  127. Samir C, Srivastava A, Daoudi M (2006) Three-dimensional face recognition using shapes of facial curves. IEEE Trans Pattern Anal Mach Intell 28:1858–1863. doi: 10.1109/TPAMI.2006.235 CrossRefGoogle Scholar
  128. Sansoni G, Trebeschi M, Docchio F (2009) State-of-the-art and applications of 3D imaging sensors in industry, cultural heritage, medicine, and criminal investigation. Sensors 9:568–601. doi: 10.3390/s90100568 CrossRefGoogle Scholar
  129. Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Akarun L (2008) Bosphorus database for 3D face analysis. In: Schouten B, Juul N, Drygajlo A, Tistarelli M (eds) Biometrics and identity management, vol 5372. Lecture notes in computer science. Springer, Berlin, pp 47–56. doi: 10.1007/978-3-540-89991-4_6
  130. Scopigno R, Andujar C, Goesele M, Lensch H (2002) 3D data acquisition. http://www.gris.informatik.tu-darmstadt.de/mgoesele/download/Scopigno-2002-3DA.pdf. Accessed Dec 20, 2013
  131. Segundo MP, Queirolo C, Bellon ORP, Silva L (2007) Automatic 3D facial segmentation and landmark detection. In: 14th international conference on image analysis and processing, pp 431–436. doi: 10.1109/ICIAP.2007.4362816
  132. Shotton J et al (2013) Real-time human pose recognition in parts from single depth images. ACM Commun 56:116–124. doi: 10.1145/2398356.2398381 CrossRefGoogle Scholar
  133. Smeets D, Claes P, Vandermeulen D, Clement JG (2010) Objective 3D face recognition: evolution, approaches and challenges. Forensic Sci Int 201:125–132. doi: 10.1016/j.forsciint.2010.03.023 CrossRefGoogle Scholar
  134. Smeets D, Claes P, Hermans J, Vandermeulen D, Suetens P (2012) A comparative study of 3-D face recognition under expression variations. IEEE Trans Syst Man Cybern Part C Appl Rev 42:710–727. doi: 10.1109/TSMCC.2011.2174221 CrossRefGoogle Scholar
  135. Smeets D, Keustermans J, Vandermeulen D, Suetens P (2013) meshSIFT: Local surface features for 3D face recognition under expression variations and partial data. Comput Vis Image Underst 117:158–169. doi: 10.1016/j.cviu.2012.10.002 CrossRefGoogle Scholar
  136. Smeets D, Keustermans J, Hermans J, Claes P, Vandermeulen D, Suetens P (2011) Symmetric surface-feature based 3D face recognition for partial data. In: International joint conference on biometrics, pp 1–6. doi: 10.1109/IJCB.2011.6117539
  137. Spreeuwers L (2011) Fast and accurate 3D face recognition. Int J Comput Vis 93:389–414. doi: 10.1007/s11263-011-0426-2 zbMATHCrossRefGoogle Scholar
  138. Srivastava A, Liu X, Hesher C (2006) Face recognition using optimal linear components of range images. Image Vis Comput 24:291–299. doi: 10.1016/j.imavis.2005.07.023 CrossRefGoogle Scholar
  139. Srivastava A, Klassen E, Joshi SH, Jermyn IH (2011) Shape analysis of elastic curves in Euclidean spaces. IEEE Trans Pattern Anal Mach Intell 33:1415–1428. doi: 10.1109/TPAMI.2010.184 CrossRefGoogle Scholar
  140. Störmer A, Rigoll G (2008) A multi-step alignment scheme for face recognition in range images. In: 15th IEEE international conference on image processing, pp 2748–2751. doi: 10.1109/ICIP.2008.4712363
  141. Szeptycki P, Ardabilian M, Liming C (2009) A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In: IEEE 3rd international conference on biometrics: theory, applications, and systems, pp 1–6. doi: 10.1109/BTAS.2009.5339052
  142. Tai Sing L (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18:959–971. doi: 10.1109/34.541406 CrossRefGoogle Scholar
  143. Tan X, Chen S, Zhou Z-H, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recogn 39:1725–1745. doi: 10.1016/j.patcog.2006.03.013 zbMATHCrossRefGoogle Scholar
  144. Tang H, Yin B, Sun Y, Hu Y (2013) 3D face recognition using local binary patterns. Signal Process 93:2190–2198. doi: 10.1016/j.sigpro.2012.04.002 CrossRefGoogle Scholar
  145. ter Haar FB, Veltkamp RC (2009) A 3D face matching framework for facial curves. Graph Models 71:77–91. doi: 10.1016/j.gmod.2008.12.003 CrossRefGoogle Scholar
  146. ter Haar FB, Veltkamp RC (2010) Expression modeling for expression-invariant face recognition. Comput Graph 34:231–241. doi: 10.1016/j.cag.2010.03.010 CrossRefGoogle Scholar
  147. Torr PHS, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78:138–156. doi: 10.1006/cviu.1999.0832 CrossRefGoogle Scholar
  148. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86CrossRefGoogle Scholar
  149. Uchida N, Shibahara T, Aoki T, Nakajima H, Kobayashi K (2005) 3D face recognition using passive stereo vision. In: IEEE international conference on image processing, pp II-950–II-953. doi: 10.1109/ICIP.2005.1530214
  150. Unsang P, Yiying T, Jain AK (2008) Face recognition with temporal invariance: A 3D aging model. In: 8th IEEE international conference on automatic face and gesture recognition, pp 1–7. doi: 10.1109/AFGR.2008.4813408
  151. Veltkamp RC et al. (2011) SHREC’11 track: 3D face models retrieval. In: 4th Eurographics conference on 3D object retrieval, Llandudno, UK, Eurographics Association, pp 89–95. doi: 10.2312/3dor/3dor11/089-095
  152. Vezzetti E, Marcolin F (2012) 3D human face description: landmarks measures and geometrical features. Image Vis Comput 30:698–712. doi: 10.1016/j.imavis.2012.02.007 CrossRefGoogle Scholar
  153. Vijayan V et al. (2011) Twins 3D face recognition challenge. In: International joint conference on biometrics, pp 1–7. doi: 10.1109/IJCB.2011.6117491
  154. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE computer society conference on computer vision and pattern recognition, pp 511–518. doi: 10.1109/CVPR.2001.990517
  155. Wang Y, Pan G, Wu Z, Wang Y (2006) Exploring facial expression effects in 3D face recognition using partial ICP. In: Narayanan PJ, Nayar S, Shum H-Y (eds) Computer vision, vol 3851. Lecture notes in computer science. Springer, Berlin, pp 581–590. doi: 10.1007/11612032_59
  156. Winkler S, Min D (2013) Stereo/multiview picture quality: overview and recent advances. Sig Process Image Commun 28:1358–1373. doi: 10.1016/j.image.2013.07.008 CrossRefGoogle Scholar
  157. Xi Z, Dellandrea E, Liming C, Kakadiaris IA (2011) Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model. IEEE Trans Syst Man Cybern Part B Cybern 41:1417–1428. doi: 10.1109/TSMCB.2011.2148711 CrossRefGoogle Scholar
  158. Xiaoguang L, Jain AK, Colbry D (2006) Matching 2.5D face scans to 3D models. IEEE Trans Pattern Anal Mach Intell 28:31–43. doi: 10.1109/TPAMI.2006.15 CrossRefGoogle Scholar
  159. Xiaoguang L, Jain AK (2006) Deformation modeling for robust 3D face matching. In: IEEE computer society conference on computer vision and pattern recognition, pp 1377–1383. doi: 10.1109/CVPR.2006.96
  160. Xiaoguang L, Jain AK (2008) Deformation modeling for robust 3D face matching. IEEE Trans Pattern Anal Mach Intell 30:1346–1357. doi: 10.1109/TPAMI.2007.70784 CrossRefGoogle Scholar
  161. Xu C, Tan T, Wang Y, Quan L (2006) Combining local features for robust nose location in 3D facial data. Pattern Recogn Lett 27:1487–1494. doi: 10.1016/j.patrec.2006.02.015 CrossRefGoogle Scholar
  162. Xueqiao W, Qiuqi R, Yue M (2010a) 3D face recognition using corresponding point direction measure and depth local features. In: IEEE 10th international conference on signal processing, pp 86–89. doi: 10.1109/ICOSP.2010.5656654
  163. Xueqiao W, Qiuqi R, Yue M (2010b) A new scheme for 3D face recognition. In: IEEE 10th international conference on signal processing, pp 657–661. doi: 10.1109/ICOSP.2010.5656861
  164. Xu C, Tan T, Li S, Wang Y, Zhong C (2006a) Learning effective intrinsic features to boost 3D-based face recognition. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision, vol 3952. Lecture notes in computer science. Springer, Berlin, pp 416–427. doi: 10.1007/11744047_32
  165. Yeung-hak L, Jae-chang S (2004) Curvature based human face recognition using depth weighted Hausdorff distance. In: International conference on image processing, pp 1429–1432. doi: 10.1109/ICIP.2004.1421331
  166. Yi S, Lijun Y (2008) Automatic pose estimation of 3D facial models. In: 19th international conference on pattern recognition, pp 1–4. doi: 10.1109/ICPR.2008.4760973
  167. Yueming W, Jianzhuang L, Xiaoou T (2010) Robust 3D face recognition by local shape difference boosting. IEEE Trans Pattern Anal Mach Intell 32:1858–1870. doi: 10.1109/TPAMI.2009.200 CrossRefGoogle Scholar
  168. Yue M, Qiuqi R, Xiaoli L, Meiru M (2010) Efficient Kernel discriminate spectral regression for 3D face recognition. In: IEEE 10th international conference on signal processing, pp 662–665. doi: 10.1109/ICOSP.2010.5655733
  169. Zhang D, Lu G (2013) Biometrics: systems and applications. Springer, New York. doi: 10.1007/978-1-4614-7400-5 Google Scholar
  170. Zhang H, Zhang Y, Guo Z, Lin Z, Zhang C (2011) 3D face recognition based on principal axes registration and fusing features. Front Electr Electron Eng China 6:347–352 doi: 10.1007/s11460-011-0155-x
  171. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35:399–458. doi: 10.1145/954339.954342 CrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Visvesvaraya National Institute of TechnologyNagpurIndia

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