Skip to main content

Classification-Based Face Detection Using Compound Features

  • Conference paper
Book cover Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

Included in the following conference series:

  • 1595 Accesses

Abstract

In this paper, we propose a classification-based face detection method using compound features. Four kinds of features, namely, intensity, Gabor filter feature, decomposed gradient feature, and Harr wavelet feature are combined to construct a compound feature vector. The projection of the feature vector on a reduced feature subspace learned by principal component analysis (PCA) is used as the input of the underlying classifier, which is a polynomial neural network (PNN). The experimental results on testing a large number of images demonstrate the effectiveness of the proposed method.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Hjelmas, R., Low, B.K.: Face Detection: A Survey. Computer Vision and Image Understanding, 236–274 (2001)

    Google Scholar 

  2. Liu, C.: A Bayesian Discriminating Features Method for Face Detection. IEEE Trans. Pattern Anal. Mach. Intell., 725–740 (2003)

    Google Scholar 

  3. Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-based Face Detection. IEEE Trans. Pattern Anal. Mach. Intell., 23–28 (1998)

    Google Scholar 

  4. Sung, K.K., Poggio, T.: Example-based Learning for View-based Human Face Detection. IEEE Trans. Pattern Anal. Mach. Intell., 39–50 (1998)

    Google Scholar 

  5. Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proc. IEEE Conf. CVPR, pp. 130–136 (1997)

    Google Scholar 

  6. Huang, L.L., Shimizu, A., Hagihara, Y., Kobatake, H.: Face Detection from Cluttered Images Using a Polynomial Neural Network. Neurocomputing, 197–211 (2003)

    Google Scholar 

  7. Huang, L.L., Shimizu, A., Hagihara, Y., Kobatake, H.: Gradient Feature Extraction for Classification-based Face Detection. Pattern Recognition, 2502–2511 (2003)

    Google Scholar 

  8. Huang, L.L., Shimizu, A., Kobatake, H.: Classification-based Face Detection Using Gabor Filter Based Features. In: Proc. IEEE Conf. On Face and Gesture Recognition, pp. 397–402 (2004)

    Google Scholar 

  9. Tokunaga, H., Huang, L.L., Shimizu, A., Hagihara, Y., Kobatake, H.: Facial Characteristics Extraction Using Wavelet Transform and Its Application to Face Detection. In: Proc. Forum on Information Technology, Japan, vol. 31 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Huang, L., Shimizu, A., Kobatake, H. (2005). Classification-Based Face Detection Using Compound Features. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_17

Download citation

  • DOI: https://doi.org/10.1007/11427445_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics