Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10116))

Included in the following conference series:

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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)

    Article  Google Scholar 

  2. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

  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. Martínez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–38 (1998)

    Article  Google Scholar 

  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. Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 15(10), 1042–1052 (1993)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machine (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm

  17. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2000)

    Book  MATH  Google Scholar 

  18. Viola, P., Jones, M.: Robust real-time object detection. J. Comput. Vis. 57(2), 137–154 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology-Madras, Chennai, India

    Shreyank N. Gowda

Authors
  1. Shreyank N. Gowda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shreyank N. Gowda .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Gowda, S.N. (2017). Fiducial Points Detection of a Face Using RBF-SVM and Adaboost Classification. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10116. Springer, Cham. https://doi.org/10.1007/978-3-319-54407-6_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54407-6_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54406-9

  • Online ISBN: 978-3-319-54407-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation