Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5227))

Included in the following conference series:

Abstract

This paper presents an algorithm which detects automatically the feature points in a face image. This is a fundamental task in many applications, in particular in an automatic face recognition system. Starting from a frontal face image with a plain background we have effected an image segmentation to detect the different facial components (eyebrow, eyes, nose, mouth and chin). After this we have searched for the feature points of each face component. The algorithm has been tested on 320 face images taken from the Stirling University Face Database [10]. The points extracted in this way have been used in a face recognition algorithm based on the Hough transform.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys (2003)

    Google Scholar 

  2. Heisele, B., Ho, P., Poggio, T.: Face Recognition with Support Vector Machines: Global versus Component-based Approach. In: Proceedings of IEEE International Conference on Computer Vision (ICCV 2001) (2001)

    Google Scholar 

  3. Lanzarotti, R.: Facial Features Detection and Description, Master thesis, Università degli studi di Milano (2003)

    Google Scholar 

  4. Mavrinac, A.: Competitive Learning Techniques for Color Image Segmentation. Machine Learning and Computer Vision (2007)

    Google Scholar 

  5. Lewis, J.P.: Fast Normalized Cross Correlation. Industrial Light and Magic (1995)

    Google Scholar 

  6. Hsu, C., Chang, C., Lin, C.: A Practical Guide to Support Vector Classification (2007)

    Google Scholar 

  7. Feng, G.C., Yuen, P.C.: Variance Projection Function and Its Application to Eye Detection for Human Face Recognition. Pattern Recognition Letters (1998)

    Google Scholar 

  8. Vezhnevets, V., Degtiareva, A.: Robust and Accurate Eye Contour Extraction. In: Proc. Graphicon (2003)

    Google Scholar 

  9. Kienzle, W., Bakir, G., Franz, M., Scholkopf, B.: Face Detection - Efficient and Rank Deficient. Advances in Neural Information Processing Systems (2005)

    Google Scholar 

  10. Stirling University Face Database, http://pics.psych.stir.ac.uk

  11. Feng, G.C., Yuen, P.C.: Variance Projection Function and Its Application to Eye Detection for Human Face Recognition. Pattern Recognition Letters (1998)

    Google Scholar 

  12. Wu, J., Zhou, Z.: Efficient Face Candidates Selector for Face Detection (2002)

    Google Scholar 

  13. Sung, K.K., Poggio, T.: Example-based Learning for View-based Human Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence (1998)

    Google Scholar 

  14. Mastronardi, G., Daleno, D., Bevilacqua, V., Chiaia, G.: Tecniche di identificazione personale basate sulla trasformata generalizzata di Hough applicata a nuvole di punti- Congresso Nazionale. AICA 2007 - Cittadinanza e Democrazia Digitale, Milano, September 20–21 (2007)

    Google Scholar 

  15. Wiskott, L., Fellous, J., Kruger, N., Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition (1999)

    Google Scholar 

  16. Mian, A., Bennamoun, M., Owens, R.: An Efficient Multi-modal 2D-3D Hybrid Approach to Automatic Face Recognition. IEEE Trans. Pattern Analysis and Machine Intelligence (2007)

    Google Scholar 

  17. Canny, J.: A Computational Approach to Edge Detection. IEEE Trans. PAMI 8(6) (1986)

    Google Scholar 

  18. Ciccimarra, A.: Ricerca Automatica dei punti caratteristici del volto. First Level Degree Thesis (2008)

    Google Scholar 

  19. Delac, K., Grgic, M.: A Survey of Biometric Recognition Systems (2004)

    Google Scholar 

  20. Bevilacqua, V., Cariello, L., Carro, G., Daleno, D., Mastronardi, G.: A Face Recognition System Based on Pseudo 2D HMM Applied to Neural Network Coefficients. Soft Comput. 12(7), 615–621 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

De-Shuang Huang Donald C. Wunsch II Daniel S. Levine Kang-Hyun Jo

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bevilacqua, V., Ciccimarra, A., Leone, I., Mastronardi, G. (2008). Automatic Facial Feature Points Detection. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_137

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85984-0_137

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85983-3

  • Online ISBN: 978-3-540-85984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics