Advertisement

Fiducial Points Detection of a Face Using RBF-SVM and Adaboost Classification

  • Shreyank N. GowdaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

Fiducial points are points that are used as points of reference or measure. Determining of fiducial points can be a fundamental step to recognize a face. A few important fiducial points are the eyes, lip edges, nose, chin etc. Using the fiducial points we can either obtain an outline of the entire face or develop a relationship between the fiducial points themselves to act as a medium to recognize a face. Lot of research has been ongoing in this regard. In this paper the Fiducial points and their existing relationships are studied using a Support Vector Machine with a Radial basis Function kernel. New images when tested showed a high accuracy of correct results in terms of the actual positions of the fiducial points in the image. Further classification of the fiducial points is done using an Adaboost classification to improve the accuracy.

Keywords

Face Recognition Linear Discriminant Analysis Gaussian Mixture Model Radial Basis Function Kernel Wiener Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)CrossRefGoogle Scholar
  3. 3.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)Google Scholar
  4. 4.
    Martínez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)CrossRefGoogle Scholar
  5. 5.
    Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 4–13 (2005)CrossRefGoogle Scholar
  6. 6.
    Moghaddam, B., Nastar, C., Pentland, A.: A Bayesian similarity measure for direct image matching. In: Proceedings of the 13th IEEE International Conference on Pattern Recognition, pp. 350–358 (1996)Google Scholar
  7. 7.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998)CrossRefGoogle Scholar
  8. 8.
    Heisele, B., Ho, P., Poggio, T.: Face recognition with support vector machines: global versus component-based approach. In: Proceedings of the 13th IEEE International Conference on Computer Vision, pp. 688–694 (2001)Google Scholar
  9. 9.
    Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)CrossRefGoogle Scholar
  10. 10.
    Penev, P.S., Atick, J.J.: Local feature analysis: a general statistical theory for object representation. Netw. Comput. Neural Syst. 7(3), 477–500 (1996)CrossRefzbMATHGoogle Scholar
  11. 11.
    Wiskott, L., Fellous, J.M., Kuiger, N., Von Der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)CrossRefGoogle Scholar
  12. 12.
    Waldir, S.S., Araújo, G.M., da Silva, E.A., Goldenstein, S.K.: Facial fiducial points detection using discriminative filtering on principal components. In: Proceedings of the 13th IEEE International Conference on Image Processing, pp. 2681–2684 (2010)Google Scholar
  13. 13.
    Jahanbin, S., Choi, H., Bovik, A.C.: Passive multimodal 2-D + 3-D face recognition using Gabor features and landmark distances. IEEE Trans. Inform. Forensics Secur. 6(4), 1287–1304 (2011)CrossRefGoogle Scholar
  14. 14.
    Araujo, G.M., Júnior, W.S., Silva, E.A., Goldenstein, S.K.: Facial landmarks detection based on correlation filters. In: Proceedings of the 13th IEEE International Telecommunication Symposium (2010)Google Scholar
  15. 15.
    Silva, L.E.S., Júnior, P.D.T., Santos, K.V., Junior, W.S.S.: Fiducial points detection using SVM linear classifiers. In: CS and IT-CSCP, pp. 23–31 (2014)Google Scholar
  16. 16.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machine (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm
  17. 17.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)CrossRefzbMATHGoogle Scholar
  18. 18.
    Viola, P., Jones, M.: Robust real-time object detection. J. Comput. Vis. 57(2), 137–154 (2001)CrossRefGoogle Scholar
  19. 19.
    Chen, J., Benesty, J., Huang, Y., Doclo, S.: New insights into the noise reduction Wiener filter. IEEE Trans. Audio Speech Lang. Process. 14(4), 1218–1234 (2006)CrossRefGoogle Scholar
  20. 20.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. In: Proceedings of International Conference on Audio-and Video-Based Biometric Person Authentication, pp. 90–95 (2001)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Indian Institute of Technology-MadrasChennaiIndia

Personalised recommendations