Skip to main content

Incremental Learning of Patch-Based Bag of Facial Words Representation for Online Face Recognition in Videos

  • Conference paper
  • 3457 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7674)

Abstract

Video-based face recognition is a fundamental topic in image and video analysis, and presents various challenges and opportunities. In this paper, we introduce an incremental learning approach to video-based face recognition, which efficiently exploits the spatiotemporal information in videos. Face image sequences are incrementally clustered based on their descriptors. With the quantization of the facial words, representation of the face image is generated by concatenating the histograms from regions. In the online recognition, a temporal matrix and a voting algorithm are employed to judge a face video’s identity. The proposed method achieves a 100% recognition rate performed on the Honda/UCSD database, and gives near realtime feedback. Experimental results demonstrate the effectiveness and flexibility of our proposed method.

Keywords

  • Face Recognition
  • Recognition Rate
  • Face Image
  • Vote Rate
  • Spatial Pyramid Match

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Liu, L., Wang, Y., Tan, T.: Online appearance model learning for video-based face recognition. In: Proc. CVPR, pp. 1–7 (2007)

    Google Scholar 

  2. Mian, A.: Online learning from local features for video-based face recognition. PR 44(5), 1068–1075 (2011)

    MATH  Google Scholar 

  3. Liu, X., Cheng, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. CVPR, pp. 340–345 (2003)

    Google Scholar 

  4. Lee, K., Ho, J., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. CVPR, pp. 313–320 (2003)

    Google Scholar 

  5. Gou, G., Shen, R., Wang, Y., Basu, A.: Temporal-spatial face recognition using multi-atlas and markov process model. In: Proc. ICME, pp. 1–4 (2011)

    Google Scholar 

  6. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR, Intel, Microprocessor Research Labs, p. 511 (2001)

    Google Scholar 

  7. Carnegie, R.C.: Mean-shift blob tracking through scale space. In: Proc. CVPR, pp. 234–240 (2003)

    Google Scholar 

  8. Schneider, J., Borlund, P.: Matrix comparison, part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination results. JASIST 58(11), 1586–1595 (2007)

    CrossRef  Google Scholar 

  9. Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: Proc. CVPR, pp. 1458–1465 (2005)

    Google Scholar 

  10. Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    CrossRef  Google Scholar 

  11. Ahonen, T., Matas, J., He, C., Pietikäinen, M.: Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. In: Salberg, A.-B., Hardeberg, J.Y., Jenssen, R. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 61–70. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  12. Kim, M., Kumar, S., Pavlovic, V., Rowley, H.: Face tracking and recognition with visual constraints in real-world videos. In: Proc. CVPR, pp. 1–8. IEEE (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, C., Wang, Y., Zhang, Z. (2012). Incremental Learning of Patch-Based Bag of Facial Words Representation for Online Face Recognition in Videos. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34778-8_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34777-1

  • Online ISBN: 978-3-642-34778-8

  • eBook Packages: Computer ScienceComputer Science (R0)