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A Rough Neurocomputing Approach for Illumination Invariant Face Recognition System

  • Singh KavitaEmail author
  • Zaveri Mukesh
  • Raghuwanshi Mukesh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8449)

Abstract

To surmount the issue of illumination variation in face recognition, this paper proposes a rough neurocomputing recognition system, namely, RNRS for an illumination invariant face recognition. The main focus of the proposed work is to address the problem of variations in illumination through the strength of the rough sets to recognize the faces under varying effects of illumination. RNRS uses geometric facial features and an approximation-decider neuron network as a recognizer. The novelty of the proposed RNRS is that the correct face match is estimated at the approximation layer itself based on the highest rough membership function value. On the contrary, if it is not being done at this layer then decider neuron does this against reduced number of sample faces. The efficiency and robustness of the proposed RNRS are demonstrated on different standard face databases and are compared with state-of-art techniques. Our proposed RNRS has achieved 93.56 % recognition rate for extended YaleB face database and 85 % recognition rate for CMU-PIE face database for larger degree of variations in illumination.

Keywords

Rough sets Neurocomputing Illumination variation Face recognition 

Notes

Acknowledgement

This project is supported by AICTE of India (Grant No: 8023/ RID/RPS-81/2010-11, Dated: March 31, 2011).

References

  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)CrossRefGoogle Scholar
  3. 3.
    Xiaogang, W.S., Xiaoou, T.: Bayesian face recognition using gabor features. In: Proceedings of the ACM SIGMM Workshop, pp. 70–73 (2003)Google Scholar
  4. 4.
    Alpaydin, E.: Introduction to Machine Learning. MIT press, Cambridge (2004)zbMATHGoogle Scholar
  5. 5.
    Yiu, K., Mak, M., Li, C.: Gaussian Mixture Models and Probabilistic Decision-Based Neural Networks for Pattern Classification: A Comparative Study. Hong Kong Polytechnic University, Hong Kong (1999)CrossRefGoogle Scholar
  6. 6.
    Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19, 696–710 (1997)CrossRefGoogle Scholar
  7. 7.
    Liu, C., Wechsler, H.: Probabilistic reasoning models for face recognition. In: Proceedings of Computer Vision and Pattern Recognition, pp. 827–832 (1998)Google Scholar
  8. 8.
    Sang-II, C., Chong-Ho, C.: An effective face recognition under illumination and pose variation. In: Proceedings of International Joint Conference on Neural Networks, pp. 914–919 (2007)Google Scholar
  9. 9.
    Howell, A., Buxton, H.: Face recognition using radial basis function neural networks. In: Proceedings of the British Machine Vision Conference, pp. 455–464 (1996)Google Scholar
  10. 10.
    Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 23–38 (1998)CrossRefGoogle Scholar
  11. 11.
    Garica, C., Delakis, M.: Convolution face finder: a neural architecture of fast and robust face detection. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1408–1423 (2004)CrossRefGoogle Scholar
  12. 12.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Li, S.Z., Wang, Y.: Face recognition under varying lightening conditions using self quotient image. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR), pp. 819–824 (2004)Google Scholar
  14. 14.
    Kavita, S.R., Mukeshl, Z.A., Mukesh, R.M.: Extraction of pose invariant facial features. In: Das, V.V., Vijaykumar, R. (eds.) ICT 2010. CCIS, vol. 101, pp. 535–539. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Han, L., Peters, J.F.: Rough neural fault classification of power system signals. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 396–519. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Singh, K.R., Zaveri, M.A., Raghuwanshi, M.M.: A robust illumination classifier using rough sets. In: Proceedings of Second IEEE International Conference on Computer Science and Automation Engineering, pp. 10–12 (2011)Google Scholar
  17. 17.
  18. 18.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25, 1615–1618 (2003)CrossRefGoogle Scholar
  19. 19.
    de Solar, J.R., Quinteros, J.: Illumination compensation and normalization in eigen space-based face recognition: a comparative study of different pre-processing approaches. Comput. Vis. Graph. Image Process. 42, 342–350 (1999)Google Scholar
  20. 20.
    Singh, K.R., Zaveri, M.A., Raghuwanshi, M.M.: Rough membership function-based illumination classifier for illumination invariant face recognition. Elsevier Appl. Soft Comput. 13(10), 4105–4117 (2013)CrossRefGoogle Scholar
  21. 21.
    AT & T or ORL Face Database (2010). http://www.uk.research.att.com/facedatabase.html. Accessed July 2010

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Singh Kavita
    • 1
    Email author
  • Zaveri Mukesh
    • 2
  • Raghuwanshi Mukesh
    • 3
  1. 1.Computer Technology DepartmentY.C.C.ENagpurIndia
  2. 2.Computer Engineering DepartmentS.V.N.I.TSuratIndia
  3. 3.Rajiv Gandhi College of Engineering and ResearchNagpurIndia

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