Face Recognition Using Posterior Distance Model Based Radial Basis Function Neural Networks

  • S. Thakur
  • J. K. Sing
  • D. K. Basu
  • M. Nasipuri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


The success rate of a face recognition system heavily depends on two issues, mainly, i) feature extraction method and ii) choosing/designing of a classifier to classify a new face image based on the extracted features. In this paper, we have addressed both the above issues by proposing a new feature extraction technique and a posterior distance model based radial basis function neural networks (RBFNN). First, the dimension of the face images is reduced by a new direct kernel principal component analysis (DKPCA) method. Then, the resulting face vectors are further reduced by the Fisher’s discriminant analysis (FDA) technique to acquire lower dimensional discriminant features. During classification, when the RBFNN is not so confident to classify a test image, we have introduced a statistical method called the posterior distance model (PDM) to resolve the conflict. The PDM is an approach, which takes a decision by integrating the outputs of the RBFNN and a distance measure. We call the new classifier the posterior distance model based radial basis function neural networks (PDM-RBFNN). The proposed method has been evaluated on the AT&T database. The simulation results in terms of recognition rates are found to better than some of the existing related approaches.


Face recognition Radial basis function neural networks Direct kernel principal component analysis Fisher’s discriminant analysis 


  1. 1.
    Samal, A., Iyengar, P.: Automatic recognition and analysis of human faces and facial expressions: A survey. Pattern Recognition 25, 65–77 (1992)CrossRefGoogle Scholar
  2. 2.
    Chellapa, R., Wilson, C., Sirohey, S.: Human and machine recognition of faces: A survey. J. IEEE 83, 705–741 (1995)CrossRefGoogle Scholar
  3. 3.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  4. 4.
    Tolba, A.S., El-Baz, A.H., El-Harby, A.A.: Face Recognition: A Literature Review. International Journal of Signal Processing 2, 88–103 (2006)Google Scholar
  5. 5.
    Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N.: Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. Neural Networks 14, 117–126 (2003)CrossRefGoogle Scholar
  6. 6.
    Yang, J., Frangi, A.F., Yang, J.-Y.: A new kernel Fisher discriminant algorithm with application to face recognition. Neurocomputing 56, 415–421 (2004)CrossRefGoogle Scholar
  7. 7.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 23, 711–720 (1997)CrossRefGoogle Scholar
  8. 8.
    Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Networks 13, 697–710 (2002)CrossRefGoogle Scholar
  9. 9.
    Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.: Face recognition using point symmetry distance-based RBF network. Applied Soft Computing 7, 58–70 (2007)CrossRefGoogle Scholar
  10. 10.
    Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.: High-speed face recognition using self-adaptive radial basis function neural networks. To appear in Neural Computing & Application, Springer, HeidelbergGoogle Scholar
  11. 11.
    Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.: Direct kernel PCA with RBF neural networks for face recognition. In: IEEE TENCON 2008, Hyderabad, India (2008)Google Scholar
  12. 12.
    ORL face database. AT&T Laboratories, Cambridge, UK,

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Thakur
    • 1
  • J. K. Sing
    • 2
  • D. K. Basu
    • 2
  • M. Nasipuri
    • 2
  1. 1.Department of Information TechnologyNetaji Subhas Engineering CollegeKolkataIndia
  2. 2.Department of Computer Science & EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations