High-speed face recognition using self-adaptive radial basis function neural networks

  • Jamuna Kanta Sing
  • Sweta Thakur
  • Dipak Kumar Basu
  • Mita Nasipuri
  • Mahantapas Kundu
Original Article


In this work, we have proposed a self-adaptive radial basis function neural network (RBFNN)-based method for high-speed recognition of human faces. It has been seen that the variations between the images of a person, under varying pose, facial expressions, illumination, etc., are quite high. Therefore, in face recognition problem to achieve high recognition rate, it is necessary to consider the structural information lying within these images in the classification process. In the present study, it has been realized by modeling each of the training images as a hidden layer neuron in the proposed RBFNN. Now, to classify a facial image, a confidence measure has been imposed on the outputs of the hidden layer neurons to reduce the influences of the images belonging to other classes. This process makes the RBFNN as self-adaptive for choosing a subset of the hidden layer neurons, which are in close neighborhood of the input image, to be considered for classifying the input image. The process reduces the computation time at the output layer of the RBFNN by neglecting the ineffective radial basis functions and makes the proposed method to recognize face images in high speed and also in interframe period of video. The performance of the proposed method has been evaluated on the basis of sensitivity and specificity on two popular face recognition databases, the ORL and the UMIST face databases. On the ORL database, the best average sensitivity (recognition) and specificity rates are found to be 97.30 and 99.94%, respectively using five samples per person in the training set. Whereas, on the UMIST database, the above quantities are found to be 96.36 and 99.81%, respectively using eight samples per person in the training set. The experimental results indicate that the proposed method outperforms some of the face recognition approaches.


Self-adaptive Radial basis function (RBF) neural networks Face recognition ORL database UMIST database 


  1. 1.
    Samal A, Iyengar P (1992) Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognit 25:65–77. doi:10.1016/0031-3203(92)90007-6 CrossRefGoogle Scholar
  2. 2.
    Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83:705–740. doi:10.1109/5.381842 CrossRefGoogle Scholar
  3. 3.
    Zhao W, Chellappa R, Phillops PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35:399–458. doi:10.1145/954339.954342 CrossRefGoogle Scholar
  4. 4.
    Tolba AS, El-Baz AH, El-Harby AA (2006) Face recognition: a literature review. Int J Signal Process 2:88–103Google Scholar
  5. 5.
    Chen CW, Huang CL (1992) Human face recognition from a single front view. Int J Pattern Recognit Artif Intell 6:571–593. doi:10.1142/S021800149200031X CrossRefGoogle Scholar
  6. 6.
    Brunelli R, Poggio T (1993) Face recognition: features versus template. IEEE Trans Pattern Anal Mach Intell 15:1042–1052. doi:10.1109/34.254061 CrossRefGoogle Scholar
  7. 7.
    Kamel MS, Shen HC, Wong AKC, Hong TM, Campeanu RI (1994) Face recognition using perspective invariant features. Pattern Recognit Lett 15:877–883. doi:10.1016/0167-8655(94)90149-X CrossRefGoogle Scholar
  8. 8.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86. doi:10.1162/jocn.1991.3.1.71 CrossRefGoogle Scholar
  9. 9.
    Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13:697–710. doi:10.1109/TNN.2002.1000134 CrossRefGoogle Scholar
  10. 10.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19:711–720. doi:10.1109/34.598228 CrossRefGoogle Scholar
  11. 11.
    Sing JK, Basu DK, Nasipuri M, Kundu M (2007) Face recognition using point symmetry distance-based RBF network. Appl Soft Comput 7:58–70. doi:10.1016/j.asoc.2005.02.004 CrossRefGoogle Scholar
  12. 12.
    Gharai S, Thakur S, Lahiri S, Sing JK, Basu DK, Nasipuri M, Kundu M (2006) Self-adaptive RBF neural networks for face recognition. In: Proceedings fo the 2nd international symposium on visual computing (ISVC06), Lake Tahoe, Nevada, USA. Lecture notes computer science, vol 4291. pp 353–362Google Scholar
  13. 13.
    Yang F, Paindovoine M (2003) Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification. IEEE Trans Neural Netw 14:1162–1175. doi:10.1109/TNN.2003.816035 CrossRefGoogle Scholar
  14. 14.
    Er MJ, Chen W, Wu S (2005) High-speed face recognition based on discrete cosine transform and RBF neural networks. IEEE Trans Neural Netw 16:679–691. doi:10.1109/TNN.2005.844909 CrossRefGoogle Scholar
  15. 15.
    Haddadnia J, Faez K, Ahmadi M (2003) A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition. Pattern Recognit 36:1187–1202. doi:10.1016/S0031-3203(02)00231-5 MATHCrossRefGoogle Scholar
  16. 16.
    Valentin D, Abdi H, O’Toole AJ, Cottrell GW (1994) Connectionist models of face processing: a survey. Pattern Recognit 27:1209–1230. doi:10.1016/0031-3203(94)90006-X CrossRefGoogle Scholar
  17. 17.
    Moody J, Darken CJ (1989) Fast learning in network of locally-tuned processing units. Neural Comput 1:281–294. doi:10.1162/neco.1989.1.2.281 CrossRefGoogle Scholar
  18. 18.
    Lee S, Kil RM (1991) A Gaussian potential function network with hierarchically self-organizing learning. Neural Netw 4:207–224. doi:10.1016/0893-6080(91)90005-P CrossRefGoogle Scholar
  19. 19.
    Park J, Wsandberg J (1991) Universal approximation using radial basis functions network. Neural Comput 3:246–257. doi:10.1162/neco.1991.3.2.246 CrossRefGoogle Scholar
  20. 20.
    Haykin S (1999) Neural networks a comprehensive foundation, 2nd edn. Prentice-Hall, Englewood CliffsGoogle Scholar
  21. 21.
    ORL face database. AT&T Laboratories, Cambridge, UK. http://www.cl.cam.ac.uk/Research/DTG/attarchive/facedatabase.html
  22. 22.
    Graham DB, Allinson NM (1998) Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler H, Phillips PJ, Bruce V, Fogelman-Soulie F, Huang TS (eds) Face recognition: from theory to applications, NATO ASI Series F, Computer and systems sciences, vol 163. pp 446–456Google Scholar
  23. 23.
    Ayinde O, Yang Y-H (2002) Face recognition approach based on rank correlation of Gabor-filtered images. Pattern Recognit 35:1275–1289. doi:10.1016/S0031-3203(01)00120-0 MATHCrossRefGoogle Scholar
  24. 24.
    Brennan V, Principe J (1998) Face classification using a multiresolution principal component analysis. In: Proceedings of the IEEE workshop on neural networks signal processing. pp 506–515Google Scholar
  25. 25.
    Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8:98–113. doi:10.1109/72.554195 CrossRefGoogle Scholar
  26. 26.
    Li SZ, Lu J (1999) Face recognition using the nearest feature line method. IEEE Trans Neural Netw 10:439–443. doi:10.1109/72.750575 CrossRefGoogle Scholar
  27. 27.
    Zhang B-L, Guo Y (2001) Face recognition by wavelet domain associative memory. In: Proceedings of the International symposium on intelligence multimedia, video, speech processing, pp 481–485Google Scholar
  28. 28.
    Virginia E-D (2000) Biometric identification system using a radial basis network. In: Proceedings of the 34th annual IEEE international Carnahan conference on security technology. pp 47–51Google Scholar
  29. 29.
    Wang L, Sun Y (2007) A new approach for face recognition based on SGFS and SVM. In: Proceedings of the 1st international conference on bioinformatics and biomedical engineering. pp 527–530Google Scholar
  30. 30.
    Guo G, Li SZ, Chan K (2000) Face recognition by support vector machines. In: Proceedings of the 4th IEEE international conference on automatic face and gesture recognition. pp 196–201Google Scholar
  31. 31.
    Xiong H, Swamy MNS, Ahmad MO (2005) Two-dimensional FLD for face recognition. Pattern Recognit 38:1121–1124. doi:10.1016/j.patcog.2004.12.003 CrossRefGoogle Scholar
  32. 32.
    Li W, Gang W, Liang Y, Chen W (2005) Feature selection based on KPCA, SVM and GSFS for face recognition. In: Proceedings of the ICAPR. Lecture notes on computer science, vol 3687. pp 344–350Google Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Jamuna Kanta Sing
    • 1
  • Sweta Thakur
    • 2
  • Dipak Kumar Basu
    • 1
  • Mita Nasipuri
    • 1
  • Mahantapas Kundu
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Information TechnologyNetaji Subhas Engineering CollegeKolkataIndia

Personalised recommendations