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

  • Chao Wang
  • Yunhong Wang
  • Zhaoxiang Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

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References

  1. 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. 2.
    Mian, A.: Online learning from local features for video-based face recognition. PR 44(5), 1068–1075 (2011)zbMATHGoogle Scholar
  3. 3.
    Liu, X., Cheng, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. CVPR, pp. 340–345 (2003)Google Scholar
  4. 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. 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. 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. 7.
    Carnegie, R.C.: Mean-shift blob tracking through scale space. In: Proc. CVPR, pp. 234–240 (2003)Google Scholar
  8. 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)CrossRefGoogle Scholar
  9. 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. 10.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chao Wang
    • 1
  • Yunhong Wang
    • 1
  • Zhaoxiang Zhang
    • 1
  1. 1.School of Computer Science and EngineeringBeihang UniversityChina

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