Face Recognition Using 3D Images

  • I. A. Kakadiaris
  • G. Passalis
  • G. Toderici
  • E. Efraty
  • P. Perakis
  • D. Chu
  • S. Shah
  • T. Theoharis

Abstract

In this chapter, we present advances that aid in overcoming the challenges encountered in 3D face recognition. First, we present a fully automatic 3D face recognition system, UR3D, which has been proven to be robust under variations in expressions. Second, we demonstrate how to handle pose variations. Finally, we demonstrate how the problems related to the cost and unfriendliness of 3D scanners can be mitigated through hybrid systems.

References

  1. 1.
    Al-Osaimi, F., Bennamoun, M., Mian, A.: An expression deformation approach to non-rigid 3D face recognition. Int. J. Comput. Vis. 81(3), 302–316 (2009) CrossRefGoogle Scholar
  2. 2.
    Basri, R., Jacobs, D.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003) CrossRefGoogle Scholar
  3. 3.
    Biswas, S., Aggarwal, G., Chellappa, R.: Robust estimation of albedo for illumination-invariant matching and shape recovery. IEEE Trans. Pattern Anal. Mach. Intell. 1(8), 884–899 (2008) Google Scholar
  4. 4.
    Biswas, S., Aggarwal, G., Chellappa, R.: Robust estimation of albedo for illumination-invariant matching and shape recovery. IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009) CrossRefGoogle Scholar
  5. 5.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003) CrossRefGoogle Scholar
  6. 6.
    Blanz, V., Scherbaum, K., Seidel, H.-P.: Fitting a morphable model to 3D scans of faces. In: Proc. 11th IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, 14–20 October 2007 Google Scholar
  7. 7.
    Bottino, A., Cumani, S.: A fast and robust method for the identification of face landmarks in profile images. WSEAS Trans. Comput. 7, 1250–1259 (2008) Google Scholar
  8. 8.
    Bowyer, K., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006) CrossRefGoogle Scholar
  9. 9.
    Bronstein, A., Bronstein, M., Kimmel, R.: Three-dimensional face recognition. Int. J. Comput. Vis. 64(1), 5–30 (2005) CrossRefGoogle Scholar
  10. 10.
    Bronstein, A., Bronstein, M., Kimmel, R.: Robust expression-invariant face recognition from partially missing data. In: Proc. European Conference on Computer Vision, pp. 396–408, Graz, Austria (2006) Google Scholar
  11. 11.
    Chang, K., Bowyer, K., Flynn, P.: Adaptive rigid multi-region selection for handling expression variation in 3D face recognition. In: Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 157–164, San Diego, CA, 20–25 June 2005 Google Scholar
  12. 12.
    Chang, K., Bowyer, K., Flynn, P.: Effects on facial expression in 3D face recognition. In: Proc. SPIE Biometric Technology for Human Identification II, vol. 5779, pp. 132–143, Orlando, FL (2005) Google Scholar
  13. 13.
    Chang, K., Bowyer, K., Flynn, P.J.: An evaluation of multi-modal 2D+3D face biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 619–624 (2005) CrossRefGoogle Scholar
  14. 14.
    Cootes, T., Taylor, C.: Statistical models of appearance for computer vision. Technical report, University of Manchester, October 2001 Google Scholar
  15. 15.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models—their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995) CrossRefGoogle Scholar
  16. 16.
    Cootes, T., Taylor, C., Kang, H., Petrovic, V.: Modeling facial shape and appearance. In: Handbook of Face Recognition, pp. 39–63. Springer, Berlin (2005) CrossRefGoogle Scholar
  17. 17.
    Dibeklioglu, H.: Part-based 3D face recognition under pose and expression variations. Master’s thesis, Bogazici University (2008) Google Scholar
  18. 18.
    Dibeklioglu, H., Salah, A., Akarun, L.: 3D facial landmarking under expression, pose and occlusion variations. In: Proc. 2nd International Conference on Biometrics Theory, Applications and Systems, Arlington, VA, 29 September–1 October 2008 Google Scholar
  19. 19.
    Dryden, I., Mardia, K.: Statistical Shape Analysis. Wiley, New York (1998) MATHGoogle Scholar
  20. 20.
    Efraty, B., Ismailov, E., Shah, S., Kakadiaris, I.: Profile-based 3d-aided face recognition. Pattern Recognit. (2011, in press). Corrected Proof. Available online 19 July 2011 Google Scholar
  21. 21.
    Face recognition vendor test 2006 (2006). http://www.frvt.org/FRVT2006/
  22. 22.
    Galton, F.: Numeralised profiles for classification and recognition. Nature 83, 127–130 (1910) CrossRefGoogle Scholar
  23. 23.
    Gao, Y.: Efficiently comparing face images using a modified Hausdorff distance. In: Proc. IEEE Conference on Vision, Image and Signal Processing, pp. 346–350, December 2003 Google Scholar
  24. 24.
    Gao, Y., Leung, M.: Human face profile recognition using Attributed String. Pattern Recognit. 35(2), 353–360 (2002) MATHCrossRefGoogle Scholar
  25. 25.
    Gao, Y., Leung, M.: Line segment Hausdorff distance on face matching. Pattern Recognit. 35(2), 361–371 (2002) MATHCrossRefGoogle Scholar
  26. 26.
    Gu, L., Kanade, T.: 3D alignment of face in a single image. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1305–1312, New York, NY, 17–22 June 2006 Google Scholar
  27. 27.
    Harmon, L.D., Khan, M.K., Lasch, R., Ramig, P.: Machine identification of human faces. Pattern Recognit. 2(13), 97–110 (1981) CrossRefGoogle Scholar
  28. 28.
    Husken, M., Brauckmann, M., Gehlen, S., von der Malsburg, C.: Strategies and benefits of fusion of 2D and 3D face recognition. In: Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 174–181, San Diego, CA, 20–25 June 2005 Google Scholar
  29. 29.
    Kakadiaris, I., Passalis, G., Theoharis, T., Toderici, G., Konstantinidis, I., Murtuza, N.: Multimodal face recognition: Combination of geometry with physiological information. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1022–1029, San Diego, CA, 20–25 June 2005 Google Scholar
  30. 30.
    Kakadiaris, I., Passalis, G., Toderici, G., Murtuza, M., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–649 (2007) CrossRefGoogle Scholar
  31. 31.
    Kakadiaris, I., Abdelmunim, H., Yang, W., Theoharis, T.: Profile-based face recognition. In: Proc. 8th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–8, Amsterdam, The Netherlands, 17–19 September 2008 CrossRefGoogle Scholar
  32. 32.
    Kaufman, G., Breeding, K.: The automatic recognition of human faces from profile silhouettes. IEEE Trans. Syst. Man Cybern. 6, 113–121 (1976) MATHGoogle Scholar
  33. 33.
    L1 Identity Solutions. L1 faceit SDK Google Scholar
  34. 34.
    Lee, J., Machiraju, R., Pfister, H., Moghaddam, B.: Estimation of 3D faces and illumination from single photographs using a bilinear illumination model. In: Proc. Eurographics Symposium on Rendering, pp. 73–82, Konstanz, Germany, 29 June–1 July 2005 Google Scholar
  35. 35.
    Li, C., Barreto, O.: Profile-based 3D Face Registration and Recognition, vol. 3506, pp. 478–488. Springer, Berlin (2005). Chap. 10 Google Scholar
  36. 36.
    Lin, T., Shih, W., Chen, W., Ho, W.: 3D face authentication by mutual coupled 3D and 2D feature extraction. In: Proc. 44th ACM Southeast Regional Conference, Melbourne, FL, 10–12 March 2006 Google Scholar
  37. 37.
    Liposcak, Z., Loncaric, S.: A scale-space approach to face recognition from profiles. In: Proc. 8th International Conference on Computer Analysis of Images and Patterns, pp. 243–250, London, UK, September 1999 Google Scholar
  38. 38.
    Lu, X., Jain, A.: Multimodal facial feature extraction for automatic 3D face recognition. Technical Report MSU-CSE-05-22, Michigan State University, October 2005 Google Scholar
  39. 39.
    Lu, X., Jain, A.: Automatic feature extraction for multiview 3D face recognition. In: Proc. 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, 10–12 April 2006 Google Scholar
  40. 40.
    Lu, X., Jain, A.: Deformation modeling for robust 3D face matching. In: Proc. IEEE Computer Vision and Pattern Recognition, pp. 1377–1383, New York, NY, 17–22 June 2006 Google Scholar
  41. 41.
    Lu, X., Jain, A., Colbry, D.: Matching 2.5D face scans to 3D models. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 31–43 (2006) CrossRefGoogle Scholar
  42. 42.
    Mahoor, M., Abdel-Mottaleb, M.: Facial features extraction in color images using enhanced active shape model. In: Proc. 7th International Conference on Automatic Face and Gesture Recognition, pp. 144–148, Washington, DC, USA, 2–6 April 2006 CrossRefGoogle Scholar
  43. 43.
    Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., Medioni, G.: Performance of Geometrix ActiveIDTM 3D face recognition engine on the FRGC data. In: Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, San Diego, CA, 20–25 June 2005 Google Scholar
  44. 44.
    Mian, A., Bennamoun, M., Owen, R.: An efficient multimodal 2D-3D hybrid approach to automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(11), 1927–1943 (2007) CrossRefGoogle Scholar
  45. 45.
    Nair, P., Cavallaro, A.: Matching 3D faces with partial data. In: Proc. British Machine Vision Conference, Leeds, UK, 1–4 September 2008 Google Scholar
  46. 46.
    Pan, G., Han, S., Wu, Z., Wang, Y.: 3D face recognition using mapped depth images. In: Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 175–181, San Diego, CA, 20–25 June 2005 Google Scholar
  47. 47.
    Pan, G., Zheng, L., Wu, Z.: Robust metric and alignment for profile-based face recognition: An experimental comparison. In: Proc. 7th IEEE Workshop on Applications of Computer Vision, vol. 1, pp. 117–122, January 2005 Google Scholar
  48. 48.
    Papatheodorou, T., Rueckert, D.: 3D face recognition. In: Face Recognition, pp. 417–446. I-Tech Education and Publishing, July 2007 Google Scholar
  49. 49.
    Passalis, G., Kakadiaris, I., Theoharis, T., Toderici, G., Murtuza, N.: Evaluation of 3D face recognition in the presence of facial expressions: An annotated deformable model approach. In: Proc. IEEE Workshop on Face Recognition Grand Challenge Experiments, vol. 3, p. 171, San Diego, CA, 20–25 June 2005 Google Scholar
  50. 50.
    Perakis, P., Passalis, G., Theoharis, T., Toderici, G., Kakadiaris, I.: Partial matching of interpose 3D facial data for face recognition. In: Proc. 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, 28–30 September 2009 Google Scholar
  51. 51.
    Perakis, P., Theoharis, T., Passalis, G., Kakadiaris, I.: Automatic 3D facial region retrieval from multi-pose facial datasets. In: Proc. Eurographics Workshop on 3D Object Retrieval, pp. 37–44, Munich, Germany, 30 March–3 April 2009 Google Scholar
  52. 52.
    Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 947–954, San Diego, CA (2005) Google Scholar
  53. 53.
    Phong, B.: Illumination for computer generated pictures. Commun. ACM 18(6), 311–317 (1975) CrossRefGoogle Scholar
  54. 54.
    Pittsburgh Pattern Recognition. PittPatt face tracking & recognition software development kit (2009) Google Scholar
  55. 55.
    Riccio, D., Dugelay, J.-L.: Geometric invariants for 2D/3D face recognition. Pattern Recognit. Lett. 28(14), 1907–1914 (2007) CrossRefGoogle Scholar
  56. 56.
    Russ, T., Koch, K., Little, C.: 3D facial recognition: A quantitative analysis. In: Proc. 45th Annual Meeting of the Institute of Nuclear Materials Management, pp. 338–344, July 2004 Google Scholar
  57. 57.
    Scheenstra, A., Ruifrok, A., Veltkamp, R.C.: A survey of 3d face recognition methods. In: Proc. in Lecture Notes in Computer Science, pp. 891–899 (2005) Google Scholar
  58. 58.
    Smith, W., Hancock, E.: Estimating the albedo map of the face from a single image. In: Proc. IEEE International Conference on Image Processing, vol. 3, pp. 780–783, Genoa, Italy, 11–14 September 2005 Google Scholar
  59. 59.
    Stegmann, M.B., Gomez, D.D.: A brief introduction to statistical shape analysis. Technical report, Technical University of Denmark, March 2002 Google Scholar
  60. 60.
    Toderici, G., Passalis, G., Theoharis, T., Kakadiaris, I.: An automated method for human face modeling and relighting with application to face recognition. In: Proc. Workshop on Photometric Analysis For Computer Vision, Rio de Janeiro, Brazil, 14–21 October 2007 Google Scholar
  61. 61.
    Toderici, G., Passalis, G., Zafeiriou, S., Tzimiropoulos, G., Petrou, M., Theoharis, T., Kakadiaris, I.: Bidirectional relighting for 3D-aided 2D face recognition. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010) Google Scholar
  62. 62.
    Tsalakanidou, F., Malassiotis, S., Strintzis, M.: A 2D+3D face identification system for surveillance applications. In: Proc. IEEE International Conference on Advanced Video and Signal based Surveillance, pp. 194–199, London, UK, 5–7 September 2007 Google Scholar
  63. 63.
    U. of Notre Dame. University of Notre Dame Biometrics Database (2008). http://www.nd.edu/@cvrl/UNDBiometricsDatabase.html
  64. 64.
    UH Computational Biomedicine Lab. UHDB11 face database (2009). http://cbl.uh.edu/URxD/datasets/
  65. 65.
    UH Computational Biomedicine Lab. UHDB12 face database (2009). http://cbl.uh.edu/URxD/datasets/
  66. 66.
    URxD-PV. UHDB22: CBL database for biometrics research. Available at http://cbl.uh.edu/URxD/datasets/
  67. 67.
    Wang, Y., Zhang, L., Liu, Z., Hua, G., Wen, Z., Zhang, Z., Samaras, D.: Face relighting from a single image under arbitrary unknown lighting conditions. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1968–1984 (2009) CrossRefGoogle Scholar
  68. 68.
    Wu, C., Huang, J.: Human face profile recognition by computer. Pattern Recognit. 23, 255–259 (1990) CrossRefGoogle Scholar
  69. 69.
    Yin, L., Yourst, M.: 3D face recognition based on high-resolution 3D face modeling from frontal and profile views. In: Proc. ACM SIGMM Workshop on Biometrics Methods and Applications, pp. 1–8, New York, NY, 8 November 2003 CrossRefGoogle Scholar
  70. 70.
    Zhou, X., Bhanu, B.: Human recognition based on face profiles in video. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2005 Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • I. A. Kakadiaris
    • 1
  • G. Passalis
    • 1
    • 2
  • G. Toderici
    • 1
  • E. Efraty
    • 1
  • P. Perakis
    • 1
    • 2
  • D. Chu
    • 1
  • S. Shah
    • 1
  • T. Theoharis
    • 1
    • 2
  1. 1.Computational Biomedicine Lab, Department of Computer ScienceUniversity of HoustonHoustonUSA
  2. 2.Computer Graphics Laboratory, Department of Informatics and TelecommunicationsUniversity of AthensIlisiaGreece

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