Skip to main content
SpringerLink
Log in

Real Time Face Detection and Recognition System Using Haar-Like Feature/HMM in Ubiquitous Network Environments

  • Conference paper
Computational Science and Its Applications – ICCSA 2005 (ICCSA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3480))

Included in the following conference series:

Abstract

In this paper, a real time face detection and recognition system is introduced for applications or services in Ubiquitous network environments. The system is realized based on a Haar-like features algorithm and a Hidden Markov model (HMM) algorithm using communications between a WPS(Wearable Personal Station) 350MHz development board and a Pentium III 800 MHz main server communicating with each other by a Bluetooth wireless communication method. Through experimentation, the system identifies faces with 96% accuracy for 480 images of 48 different people and shows successful interaction between the WPS board and the main server for intelligent face recognition service in Ubiquitous network environments.

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 139.00
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. Viola, P., Jones, M.J.: Robust real-time object detection, Technical Report Series, Compaq Cambridge research Laboratory, CRL 2001/01,(February 2001)

    Google Scholar 

  2. ETRI Technical Report,WPS Test Bed specification (2004)

    Google Scholar 

  3. Park, S., Lee, J., Kim, W.: Recognition Using Haar-like fea-ture/LDA. In: Workshop on Image Processing and Image Understanding 2004 (IPIU 2004) (January 2004)

    Google Scholar 

  4. Kuranov, A., Lienhart, R., Pisarevsky, V.: An Empirical Analysis of Boosting Algorithms for Rapid Objects With an Extended Set of Haar-like Features. Intel Technical Report MRL-TR, July02-01 (2002)

    Google Scholar 

  5. Hafed, Z.M., Levine, M.D.: Face Recognition using the discrete cosine transform. International Journal of Computer Vision 43(3), 167–188 (2001)

    Article  MATH  Google Scholar 

  6. Samaria, F., Young, S.: HMM based architecture for face identification. Image and Computer Vision 12, 537–543 (1994)

    Article  Google Scholar 

  7. Nefian, A.V., Hayes, M.H.: A Hidden Markov Model for face recognition. In: ICASSP 1998, vol. 5, pp. 2721–2724 (1998)

    Google Scholar 

  8. Nefian, A.V., Hayes, M.H.: Face detection and recognition using Hidden Markov Models. In: International Conference on Image Processing (1998)

    Google Scholar 

  9. Kuo, S., Agazzi, O.: Keyword spotting in poorly printed documents using pseudo 2-D Hidden Markov Models. IEEE Transactions on Patten Analysis and Machine Intelligence 16, 842–848 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Information and Telecommunications Engineering, The University of Suwon, South Korea

    Kicheon Hong, Jihong Min, Wonchan Lee & Jungchul Kim

Authors
  1. Kicheon Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jihong Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wonchan Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jungchul Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W, T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, P.O. Box, I-06123, Perugia, Italy

    Antonio Laganà

  5. Institute of High Performance Computing, IHCP, 1 Science Park Road, 01-01 The Capricorn, Singapore Science Park II, 117528, Singapore

    Heow Pueh Lee

  6. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  7. Clayton School of IT, Monash University, 3800, Clayton, Australia

    David Taniar

  8. OptimaNumerics Ltd, P.O. Box, Belfast, United Kingdom

    Chih Jeng Kenneth Tan

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hong, K., Min, J., Lee, W., Kim, J. (2005). Real Time Face Detection and Recognition System Using Haar-Like Feature/HMM in Ubiquitous Network Environments. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424758_121

Download citation

  • DOI: https://doi.org/10.1007/11424758_121

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25860-5

  • Online ISBN: 978-3-540-32043-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation