Machine Vision and Applications

, Volume 21, Issue 3, pp 261–274 | Cite as

Using bidimensional regression to assess face similarity

  • Sarvani Kare
  • Ashok Samal
  • David Marx
Original Paper


Face recognition is the identification of humans by the unique characteristics of their faces and forms the basis for many biometric systems. In this research the problem of feature-based face recognition is considered. Bidimensional regression (BDR) is an extension of standard regression to 2D variables. Bidimensional regression can be used to determine the degree of resemblance between two planar configurations of points and for assessing the nature of their geometry. A primary advantage of this approach is that no training is needed. The goal of this research is to explore the suitability of BDR for 2D matching. Specifically, we explore if bidimensional regression can be used as a basis for a similarity measure to compare faces. The approach is tested using standard datasets. The results show that BDR can be effective in recognizing faces and hence can be used as an effective 2D matching technique.


Face recognition Landmarks Bidimensional regression 


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  1. 1.
    Brunelli R., Poggio T.: Face recognition: features versus templates. IEEE Trans. PAMI 15(10), 1042–1062 (1993)Google Scholar
  2. 2.
    Galton, F.: Personal identification and description —{I}. Nature 38(973), 173–177Google Scholar
  3. 3.
    Samal A., Iyengar P.A.: Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognit. 25(1), 65–67 (1992)CrossRefGoogle Scholar
  4. 4.
    Zhao W., Chellappa R., Rosenfeld A., Phillips P.J.: Face recognition: a literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  5. 5.
    Oziem, D.J.: Face recognition techniques and the implications of facial transformations. PhD Dissertation, University of Bristol (2002)Google Scholar
  6. 6.
    Wiskott L., Fellous J.M., Kruger N., Vonder M.C.: Face recognition by elastic bunch graph matching. IEEE Spectr. 19(7), 775–779 (1997)Google Scholar
  7. 7.
    Duane, M.B.: Face recognition 101—the technology and its applications. (2001)
  8. 8.
    Tobler W.: Bidimensional regression. Geogr. Anal. 26, 187–212 (1994)Google Scholar
  9. 9.
    Alinda F., Bernd K.: Bidimensional regression: assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets. Psychol. Methods 8(4), 468–491 (2003)CrossRefGoogle Scholar
  10. 10.
    Tobler, W.: Comparing figures by regression. In: ACM SIGGRAPH Computer Graphics. Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, vol. 12(3) (1978)Google Scholar
  11. 11.
    Tobler, W.: Durer transforms: a research proposal.
  12. 12.
    Kaya Y., Kobayashi K.: a Basis Study on Human Face Recognition. Academic Press, Orlando (1971)Google Scholar
  13. 13.
    Bichsel, M.: Strategies of robust object recognition for automatic identification of human faces. Ph.D. thesis, Eidgenossischen, Technischen Hochschule, Zurich (1991)Google Scholar
  14. 14.
    Craw I., Costen N., Kato T., Akamatsu S.: How should we represent faces for automatic recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(8), 725–736 (1999)CrossRefGoogle Scholar
  15. 15.
    Campadelli, P., Lanzarotti, R., Savazzi, C.: A feature-based face recognition system. In: IEEE Proceedings of International Conference on Image Analysis and Processing, pp. 68–73 (2003)Google Scholar
  16. 16.
    Lanitis A., Taylor C.J., Cootes T.F.: Automatic face identification system using flexible appearance models. Image Vis. Comput. 13(5), 393–401 (1995)CrossRefGoogle Scholar
  17. 17.
    Cootes, T., Edwards, G., Taylor, C.: Active appearance models. ECCV (1998)Google Scholar
  18. 18.
    Edwards, G., Cootes, T., Taylor, C.: Face recognition using active appearance models. ECCV (1998)Google Scholar
  19. 19.
    Penev P.S., Atick J.J.: Local feature analysis: a general statistical theory for object representation. Network Comput. Neural Syst. 7(3), 477–500 (1996)zbMATHCrossRefGoogle Scholar
  20. 20.
    Kong S.G., Heo J., Abidi B.R., Paik J., Abidi M.A.: Recent advances in visual and infrared face recognition—a review. Comput. Vis. Image Underst. 97, 103–135 (2005)CrossRefGoogle Scholar
  21. 21.
    Wikott L., Fellous J.-M., Krüger N., Malsburg C.v.d.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)CrossRefGoogle Scholar
  22. 22.
    Chellappa R., Wilson C., Sirohey S.: Human and machine recognition of faces: a survey. Proc. IEEE 83(5), 705–740 (1995)CrossRefGoogle Scholar
  23. 23.
    Kirby M., Sirvoich L.: Application of the karhunen loeve procedure for the characterization of human faces. Proc. IEEE 12(1), 103–108 (1990)Google Scholar
  24. 24.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  25. 25.
    Etemad K., Chellappa R.: Discriminant analysis for recognition of human face images. J. Opt. Soc. Am. A 14, 1724–1733 (1997)CrossRefGoogle Scholar
  26. 26.
    Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: Proceedings of the 3rd International Conference on Face and Gesture Recognition, 14–16 April 1998, p. 336 (1998)Google Scholar
  27. 27.
    Kung S.Y., Taur J.S.: Decision-based neural networks with signal/image classification applications. IEEE Trans. Neural Netw. 6, 170–181 (1995)CrossRefGoogle Scholar
  28. 28.
    Lin S.H., Kung S.Y., Lin L.J.: Face recognition/detection by probabilistic decision-based neural network. IEEE Trans. Neural Netw. 8, 114–132 (1997)CrossRefGoogle Scholar
  29. 29.
    Okada, K., Steffans, J., Maurer, T., Hong, H., Elagin, E., Neven, H., Malsburg, V.v.d. (1998) The Bochum/USC Face Recognition System and how it fared in the FERET Phase III Test. In Face Recognition: From Theory to Applications, Wechsler, H., Phillips, P.J., Bruce, V., Soulie, F.F., Huang, T.S., (eds.) Springer-Verlag, Berlin, Germany, pp. 186–205Google Scholar
  30. 30.
    Kanade, T.: Picture processing system by computer complex and recognition of human faces. PhD Thesis, Kyoto University (1973)Google Scholar
  31. 31.
    Goldstein A.J., Harmon L.D., Lesk A.B.: Identification of human faces. Proc. IEEE 59(5), 748–760 (1971)CrossRefGoogle Scholar
  32. 32.
    Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: global vesus component-based approach. In: Proceedings, IEEE International Conference on Computer Vision (ICCV2001), pp. 688–694 (2001)Google Scholar
  33. 33.
    Hsu, R., Jain, A.K.: Face modeling for recognition. In: Proceedings of the IEEE International Conference Image Processing, pp. 693–696 (2001)Google Scholar
  34. 34.
    Lanzarotti, R., Borghese, N.A., Campadelli, P.: Automatic features detection for overlapping face images on their 3D range models. In: Proceedings of the IEEE International Conference on Image Analysis and Processing, pp. 316–321 (2001)Google Scholar
  35. 35.
    Smeraldi F., Bigun J.: Retinal vision applied to facial features detection and face authentication. Pattern Recognit. Lett. 23, 463–475 (2002)zbMATHCrossRefGoogle Scholar
  36. 36.
    Campadelli, P., Casiraghi, E., Lanzarotti, R.: Detection of facial features. In: Marinaro, M., Tagliaferri, R. (eds.) Thirteenth Italian Workshop on Neural Nets, Lecture Notes in Computer Science, vol. 2486, pp. 124–131 (2002)Google Scholar
  37. 37.
    Elena, C., Lanzarottiandm, R., Lipori, G.: A face detection system based on color and support vector machines. In: Marinaro, M., Tagliaferri, R. (eds.) Flourteenth Italian Workshop on Neural Nets. Lecture Notes in Computer Science, vol. 2859, pp. 113–120 (2003)Google Scholar
  38. 38.
    Campadelli, P., Lanzarotti, R., Savazzi, C.: A feature based face recognition system. In: Proceedings of International Conference on Image Analysis and Processing, pp. 68–73 (2003)Google Scholar
  39. 39.
    Arca, S., Campadelli, P., Lanzarotti, P.: A face recognition system based on local feature analysis. In: International Conference on Audio- and Video-based Biometric Person Authentication. Lecture Notes in Computer Science, vol. 2688, pp. 182–189 (2003)Google Scholar
  40. 40.
    Zhu Z., Ji Q.: Robust real-time eye detection and tracking under variable lighitng conditions and various face orientations, in the special issue on eye detection and tracking. Comput. Vis. Image Underst. 38(1), 124–154 (2005)CrossRefGoogle Scholar
  41. 41.
    Phillips P.J., Moon H., Rizvi S.A., Rauss P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  42. 42.
    Farkas L.G.: Anthropometry of the Head and Face. Raven Press, New York (1994)Google Scholar
  43. 43.
    Shi, J., Samal, A., Marx, D.: Improving face recognition efficiency using landmarks and their geometry. IEEE Trans. PAMI (2004, submitted)Google Scholar
  44. 44.
    Phillips, P.J., Grother, P.J., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: Face recognition vendor test 2002: evaluation report. (2003)

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.University of Nebraska-LincolnLincolnUSA

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