Skip to main content
Log in

A robust facial feature detection on mobile robot platform

  • Short Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Human face analysis on the mobile robot vision system should cope with difficult problems such as face pose variations, illumination changes, and complex backgrounds, in which problems are mainly induced from the movement of its platform. In this paper, in order to overcome such problems, an efficient facial feature detection approach based on local image region and direct pixel-intensity distributions is presented. We propose two novel concepts; the directional template for evaluating intensity distributions and the edge-like blob map image with multiple strength intensity. Using this blob map image, we show that the locations of major facial features—two eyes and a mouth—can be reliably estimated. Without the boundary information of facial area, final candidate face region is determined by both obtained locations of facial features and weighted correlations with stored facial templates.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Hsu R.L., Abdel-Mottaleb M., Jain A.K.: Face detection in color images. IEEE Trans. Pattern Anal. Mach. Intell. 24, 696–706 (2002)

    Article  Google Scholar 

  2. Nikolaidis A., Pitas I.: Facial feature extraction and pose determination. Pattern Recognit. 33, 1783–1791 (2000)

    Article  Google Scholar 

  3. Yang, M.H., Ahuja, N., Kriegman, D.: Mixtures of linear subspaces for face detection. In: Proceedings of Fourth Int’l Conf. Automatic Face and Gesture Recognition, pp. 70–76 (2000)

  4. Yin, J., Li, Y., Li, J.: Face feature extraction based on principle discriminant information analysis. In: Proceedings of 2008 IEEE International Conference on Automation and Logistics, pp. 1580–1584 (2007)

  5. Wang Y., Chua C.S., Ho Y.K.: Facial feature detection and face recognition from 2D and 3D images. Pattern Recognit. Lett. 23, 1191–1202 (2002)

    Article  MATH  Google Scholar 

  6. Chung, Y., Jung, S., Moon, K.: Face feature extraction using elliptical model based background deletion and generalized FEM. In: Proceedings of IEEE International Conference on Signal-Image Technologies and Internet-Based System, pp. 757–762 (2007)

  7. Han, Y., Yin, J., Li, J.: Human face feature extraction and recognition base on SIFT. In: Proceedings of International Symposium on Computer Science and Computational Technology, pp. 719–722 (2008)

  8. Yow K.C., Cipolla R.: Feature-based human face detection. Image Vis. Comput. 15(9), 713–735 (1997)

    Article  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. Ryu Y., Oh S.: Automatic extraction of eye and mouth fields from a face image using eigenfeatures and multilayer perceptrons. Pattern Recognit. 34, 2459–2466 (2001)

    Article  MATH  Google Scholar 

  11. Moghaddam B., Pentland A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 696–710 (1997)

    Article  Google Scholar 

  12. Xingming, Z., Huangyuan, Z.: An illumination independent eye detection algorithm. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 392–395 (2006)

  13. Fröba, B., Küblbeck, C.: Robust face detection at video frame rate based on edge orientation features. In: Proceedings of the 5th Int’l Conf. Automatic Face and Gesture Recognition, pp. 327–332 (2002)

  14. Gao Y., Leung M.K.H.: Face recognition using line edge map. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 764–779 (2002)

    Article  Google Scholar 

  15. Shih F.Y., Chuang C.F.: Automatic extraction of head and face boundaries and facial features. Inform. Sci. 158, 117–130 (2004)

    Article  Google Scholar 

  16. Yang M.H., Kriegman D., Ahuja N.: Detecting face in Images: survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 33–58 (2002)

    Google Scholar 

  17. Hong, D., Ruohe, Y., Kunhui, L.: Research on Face Recognition Based on PCA. In: Proceedings of Future Information Technology and Management Engineering, pp. 29–32 (2008)

  18. Heisele, B., Koshizen, T.: Components for face recognition. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), pp. 153–158 (2004)

  19. Zhou Z.H., Geng X.: Projection functions for eye detection. Pattern Recognit. 37(5), 1049–1056 (2004)

    Article  MATH  Google Scholar 

  20. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the Hausdorff distance. In: Proceedings of the 3rd Int’l Conf. on Audio- and Video-based biometric person Authentication (AVBPA), pp. 90–95 (2001)

  21. Leon-Garcia A.: Probability and random processing for electrical engineering. Addison-Wesley Publishing Company, Reading (1994)

    Google Scholar 

  22. The BioID face database: http://www.bioid.com/downloads/facedb/facedatabase.html

  23. Wu J., Zhou Z.H.: Efficient face candidates selector for face detection. Pattern Recognit. 36, 1175–1186 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chang-Woo Park.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Park, CW., Lee, T. A robust facial feature detection on mobile robot platform. Machine Vision and Applications 21, 981–988 (2010). https://doi.org/10.1007/s00138-009-0224-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-009-0224-9

Keywords

Navigation