Skip to main content

Study of Face Recognition Technology Based on STASM and Its Application in Video Retrieval

  • Conference paper
Computational Intelligence, Networked Systems and Their Applications (ICSEE 2014, LSMS 2014)

Abstract

The gradual perfection of video retrieval technology has a positive effect in maintaining public order. However, with the improving complexity of monitoring environment, the increase of related video data requires further improvement to the efficiency of video retrieval technology. Video retrieval technology aiming at processing massive video data is needed urgently and it has become hot research subject in multimedia retrieval area. In this paper, the application of face recognition technology in video retrieval is discussed. To improve the retrieval efficiency, STASM algorithm based on OpenCV software platform is designed. The research involves the acquisition of video image frame data, face recognition and detection. Experimental results demonstrate the effectiveness and efficiency of the algorithms.

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 39.99
Price excludes VAT (USA)
  • Available as 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tan, H.C., Zhang, Y.J.: Computing Eigenface from Edge Images for Face Recognition Based on Hausdorff Distance. In: Proceedings of the 4th International Conference on Image and Graphics, Chengdu, China: [s. n.] (2007)

    Google Scholar 

  2. Barkan, O., Weill, J., Wolf, L., Aronowitz, H.: Fast high dimensional vector multiplication face recognition. In: ICCV (2013)

    Google Scholar 

  3. Simonyan, K., Parkhi, O.M., Vedaldi, A., Zisserman, A.: Fisher vector faces in the wild. In: BMVC (2013)

    Google Scholar 

  4. Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic matching for pose variant face verification. In: CVPR (2013)

    Google Scholar 

  5. Cootes, T.F., Edwards, G., Taylor, C.J.: Comparing Active Shape Models with Active Appearance Models. In: Proceedings of British Machine Vision Conference (1999)

    Google Scholar 

  6. Kong, J., Han, C.: Content-based video retrieval system research. In: IEEE Conference on Computer Science and Information Technology (2010)

    Google Scholar 

  7. Smeulders, Worring, M., Santini, S.: Content-based image retrieval at the end of the early years. EEE Trans. Pattern Anal. Machine Intel. (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, C., Chen, K., Xu, Y. (2014). Study of Face Recognition Technology Based on STASM and Its Application in Video Retrieval. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45261-5_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45260-8

  • Online ISBN: 978-3-662-45261-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics