Skip to main content
SpringerLink
Log in

Improving mixture of experts for view-independent face recognition using teacher-directed learning

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper we develop a new learning method, called teacher-directed learning (TDL), for mixture of experts (ME) to perform view-independent face recognition. In the basic form of ME the problem space is automatically divided into several subspaces for the experts, and the outputs of experts are combined by a gating network. In our proposed method, the ME is directed to adapt to a particular partitioning corresponding to predetermined views. To do this, we apply a new learning method to ME, called TDL, in a way that according to the pose of the input training sample, only the weights of the corresponding experts are updated. We apply TDL to MEs, composed of MLP experts and a radial basis function gating network, with different representation schemes: global, single-view and overlapping eigenspace. We test them with previously intermediate unseen views of faces. The experimental results support our claim that directing the experts to a predetermined partitioning of the face space improves the performance of the conventional ME for view-independent face recognition. Comparison with some of the most related methods indicates that the proposed model yields excellent recognition rate in view-independent face recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Belhumeur P.N., Hespanha J., Kriegman D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. (Special Issue on Face Recognition) 17(7), 711–720 (1997)

    Article  Google Scholar 

  2. Beymer, D.J.: Face recognition under varying pose. Technical Report 1461, MIT AI Lab, Massachusetts Institute of Technology, Cambridge, MA (1993)

  3. Bowyer K.W., 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)

    Article  Google Scholar 

  4. Chen K., Xu L., Chi H.: Improved learning algorithms for mixture of experts in multiclass classification. Neural Netw. 12(9), 1229–1252 (1999)

    Article  Google Scholar 

  5. Cootes T.F., Edwards J.F., Taylor C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

    Article  Google Scholar 

  6. Czyz, J., Kittler, J., Vandendorpe, L.: Combining face verification experts. In: Proceedings of International Conference Pattern Recognition, vol. 2, pp. 20028–20031 (2002)

  7. Dailey M.N., Cottrell G.W.: Organization of face and object recognition in modular neural networks. Neural Netw. 12, 1053–1073 (1999)

    Article  Google Scholar 

  8. Du S., Ward R.K.: Face recognition under pose variations. J. Frankl. Inst. 343, 596–613 (2006)

    Article  MATH  Google Scholar 

  9. Ebrahimpour R., Kabir E., Esteky H., Yousefi M.R.: A mixture of multilayer perceptron experts network for modeling face/nonface recognition in cortical face processing regions. Intell. Autom. Soft Comput. 14(2), 145–156 (2008)

    Google Scholar 

  10. Ebrahimpour R., Kabir E., Esteky H., Yousefi M.R.: View-independent face recognition with mixture of experts. Neurocomputing 71, 1103–1107 (2008)

    Article  Google Scholar 

  11. Ebrahimpour R., Kabir E., Yousefi M.R.: Teacher-directed learning with mixture of experts for view-independent face recognition. Lect. Notes Comput. Sci. 4362, 601–611 (2007)

    Article  Google Scholar 

  12. Ebrahimpour R., Kabir E., Yousefi M.R.: View-based eigenspaces with mixture of experts for view-independent face recognition. Lect. Notes Comput. Sci. 4472, 131–140 (2007)

    Article  Google Scholar 

  13. Ghaderi, R.: Arranging simple neural networks to solve complex classification problems. Ph.D. thesis, Surrey University (2000)

  14. Goldman, S., Rivest, R., Schapire, R.: Learning binary relations and total orders. In: Proceedings of the 30th Annual IEEE Symposium on Foundations of Computer Science, pp. 46–51 (1989)

  15. Goldman, S.: Learning binary relations, total orders, and read-once formulas. Technical Report 483, MIT Laboratory for Computer Science (1990)

  16. Goldman, S., Kearns, M.: On the complexity of teaching. In: Proceedings of the 4th Annual ACM Workshop on Computational Learning Theory, pp. 303–314 (1991)

  17. Goldman, S., Mathias, D.: Teaching a smart learner. In: Proceedings of the 6th Annual ACM Workshop on Computational Learning Theory, pp. 67–76 (1993)

  18. Goldman S.A., Mathias H.D.: Teaching a smarter learner. J. Comput. Syst. Sci. 52(2), 255–267 (1996)

    Article  MathSciNet  Google Scholar 

  19. Goodband J.H., Haasa O.C.L., Millsb J.A.: A mixture of experts committee machine to design compensators for intensity modulated radiation therapy. Pattern Recognit. 39, 1704–1714 (2006)

    Article  MATH  Google Scholar 

  20. Guler I., Ubeyli E.D.: A modified mixture of experts network structure for ECG beats classification with diverse features. Eng. Appl. Artif. Intell. 18, 845–856 (2005)

    Article  Google Scholar 

  21. Haykin S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  22. Hong X., Harris C.J.: A mixture of experts network structure construction algorithm for modelling and control. Appl. Intell. 16(1), 59–69 (2002)

    Article  MATH  Google Scholar 

  23. Hong L., Jain A.: Integrating faces and fingerprints for personal identification. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1295–1307 (1998)

    Article  Google Scholar 

  24. Jackson, J., Tompkins, A.: A computational model of teaching. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 319–326 (1992)

  25. Jacobs R.A., Jordan M.I., Nowlan S.E., Hinton G.E.: Adaptive mixture of experts. Neural Comput. 3, 79–87 (1991)

    Article  Google Scholar 

  26. Kamimura R.: Teacher-directed learning with Gaussian and sigmoid activation functions. Lect. Notes Comput. Sci. 3316, 530–536 (2004)

    Article  Google Scholar 

  27. Kim T., Kittler J.: Design and fusion of pose-invariant face identification experts. IEEE Trans. Circuits Syst. Video Technol. 16(9), 1096–1106 (2006)

    Article  Google Scholar 

  28. Kirby M., Sirovich L.: Application of the KL procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    Article  Google Scholar 

  29. Kittler, J., Ballette, M., Czyz, J., Roli, F., Vandendorpe, L.: Enhancing the performance of personal identity authentication systems by fusion of face verification experts. In: Proceedings of IEEE International Conference on Multimedia Expo, vol. 2, pp. 581–584 (2002)

  30. Kittler J., Hatef M., Duin R.P.W., Matas J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  31. Kuncheva L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)

    Book  MATH  Google Scholar 

  32. Kuncheva L., Bezdek J.C., Duin R.P.W.: Decision templates for multiple classifier fusion: an experimental comparison. Pattern Recognit. 34, 299–314 (2001)

    Article  MATH  Google Scholar 

  33. Lu J., Plataniotis K.N., Venetsanopoulos A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Netw. 14(1), 117–126 (2003)

    Article  Google Scholar 

  34. Mathias, H.D.: DNF—if you can’t learn ‘em, teach ‘em: an interactive model of teaching. In: Proceedings of 8th Annu. Conf. on Comput. Learning Theory, pp. 222–229 (1995)

  35. Moghaddam B., Pentland A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 696–710 (1997)

    Article  Google Scholar 

  36. Murase H., Nayar S.K.: Visual learning and recognition of 3-D objects from appearance. Int. J. Comput. Vis. 14, 5–24 (1995)

    Article  Google Scholar 

  37. Natarajan, B.K.: On learning Boolean functions. In: Proceedings of the 19th Annual ACM Symposium on Theory of Computing, pp. 296–304 (1987)

  38. Pandya A.S., Macy R.B.: Pattern Recognition with Neural Networks in C++. CRC Press, Boca Raton (1996)

    Google Scholar 

  39. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, pp. 84–91 (1994)

  40. Rivest, R., Lin, Y.L.: Being taught can be faster than asking questions. In: Proceedings of 8th ACM Conference on Computational Learning Theory, pp. 144–151 (1995)

  41. Salzberg, S., Delcher, A., Heath, D., Kasif, S.: Learning with a helpful teacher. In: Proceedings of IJCAI-91, pp. 705–711 (1991)

  42. Sehad A., Hocini H., Hadid A., Djeddi M., Ameur S.: Face recognition under varying views. Lect. Notes Comput. Sci. 1811, 258–267 (2000)

    Article  Google Scholar 

  43. Shakhnarovich G., Moghaddam B.: Face recognition in subspaces. In: Li, S.Z., Jain, A.K. (eds) Handbook of Face Recognition, Springer-Verlag, New York (2004)

    Google Scholar 

  44. Shinohara A., Miyano S.: Teachability in computational learning. New Gener. Comput. 8, 337–347 (1991)

    Article  MATH  Google Scholar 

  45. Sim T., Baker S., Bsat M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1615–1618 (2003)

    Article  Google Scholar 

  46. Sirovich L., Kirby M.: Low-dimensional procedure for characterization of human faces. J. Opt. Soc. Am. 4, 519–524 (1987)

    Article  Google Scholar 

  47. Tan X., Chen S., Zhou Z.H., Zhang F.: Face recognition from a single image per person: a survey. Pattern Recognit. 39, 1725–1745 (2006)

    Article  MATH  Google Scholar 

  48. Tang, H., Lyu, M., King, I.: Face recognition committee machine. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 837–840 (2003)

  49. Tolba A.S., El-Baz A.H., El-Harby A.A.: Face recognition: a literature review. Int. J. Signal Process. 2(2), 88–103 (2005)

    Google Scholar 

  50. Tolba A.S., Abu-Rezq A.N.: Combined classifiers for invariant face recognition. Pattern Anal. Appl. 3(4), 289–302 (2000)

    Article  MathSciNet  Google Scholar 

  51. Turk M., Pentland A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  52. Ubeyli E.D., Guler I.: Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models. Comput. Biol. Med. 35, 405–420 (2005)

    Article  Google Scholar 

  53. Ullman S., Bart E.: Recognition invariance obtained by extended and invariant features. Neural Netw. 17, 833–848 (2004)

    Article  MATH  Google Scholar 

  54. Wiskott L., Fellous J.M., Von Der Malsburg C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19, 775–779 (1997)

    Article  Google Scholar 

  55. Xu L., Krzak A., Suen C.Y.: Methods of combining multiple classifiers and their application to handwriting recognition. IEEE Trans. Syst. Man Cybern. 22(3), 418–435 (1992)

    Article  Google Scholar 

  56. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. ACM Comput. Surv. 399–458 (2003)

  57. Zhang, W., Shan, S., Gao, W., Chang, Y., Cao, B., Yang, P.: Information fusion in face identification. In: Proceedings of International Conference Pattern Recognition, vol. 3, pp. 950–953 (2004)

Download references

Author information

Author notes

  1. Mohammad Reza Yousefi

    Present address: Max Planck Institute for Computer Science, Saarbruecken, Germany

Authors and Affiliations

  1. School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics, P.O. Box 19395-5746, Niavaran, Tehran, Iran

    Reza Ebrahimpour & Mohammad Reza Yousefi

  2. Department of Electrical and Computer Engineering, Shahid Rajaee University, Tehran, Iran

    Reza Ebrahimpour & Mohammad Reza Yousefi

  3. Department of Electrical Engineering, Tarbiat Modarres University, P.O. Box 14115-143, Tehran, Iran

    Ehsanollah Kabir

Authors
  1. Reza Ebrahimpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ehsanollah Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Reza Yousefi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Reza Ebrahimpour.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ebrahimpour, R., Kabir, E. & Yousefi, M.R. Improving mixture of experts for view-independent face recognition using teacher-directed learning. Machine Vision and Applications 22, 421–432 (2011). https://doi.org/10.1007/s00138-009-0232-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-009-0232-9

Keywords

Search

Navigation