Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Face and Facial Expression Recognition from Real World Videos

FFER 2014: Face and Facial Expression Recognition from Real World Videos pp 3–10Cite as

Probabilistic Elastic Part Model for Real-World Face Recognition

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8912))

Abstract

The ever popularity of online social media, and the ubiquity of video cameras, provide an unique source of information that is otherwise not available for military, security, and forensics applications. As a result, robust recognition of the faces presented in these unconstrained images and videos became an emerging need. For example, both the Vancouver (Canada) Riots 2011, and the tragedy Boston Bombing 2013, called for robust facial recognition technologies to identify the suspects from low quality images and videos from unconstrained sources.

In this paper, we briefly summarize our work on probabilistic elastic part model, which produces a pose invariant and compact representation for both image and video faces. The model produces a fixed dimension representation no matter how many frames a video face contains. This allows the representations produced from video faces of arbitrary frame numbers to be directly compared without the need of computationally expensive frame-to-frame matching. The probabilistic elastic part model produces state-of-the-art results in several real-world face recognition benchmarks, which we will also briefly discuss.

This is a preview of subscription content, 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   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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Grother, P.J., Quinn, G.W., Phillips, P.J.: Mbe 2010: report on the evaluation of 2d still-image face recognition algorithms. Technical Report NISTIR 7709, National Institute of Standards and Technology (2010)

    Google Scholar 

  2. Beveridge, R., Flynn, P., Philips, J., Zhang, H., Liong, V.E., Lu, J., Angeloni, M., Pereira, T., Li, H., Hua, G., Struc, V., Krizaj, J.: The ijcb 2014 pasc video face and person recognition competition. In: Submitted to International Joint Conf. on Biometrics, Clearwater, FL (2014)

    Google Scholar 

  3. Li, H., Hua, G., Lin, Z., Shen, X., Brandt, J.: Eigen-pep for video face recognition. In: Proc. The Twelvth Asian Conference on Computer Vision, Singapore (2014)

    Google Scholar 

  4. Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic matching for pose variant face verification. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Portland, Oregon (2013)

    Google Scholar 

  5. Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic part model for unsupervised face detector adaptation. In: Proc. IEEE International Conference on Computer Vision, Sydney, Australia (2013)

    Google Scholar 

  6. Li, H., Hua, G.: Probabilistic elastic part model: a pose-invariant representation for real-world face verification. Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence (2014) (Under Review)

    Google Scholar 

  7. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proc. of IEEE Conf. on Computer Vision and Patter Recognition, pp. 586–591 (1991)

    Google Scholar 

  8. Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible lighting conditions. In: CVPR, pp. 270–277 (1996)

    Google Scholar 

  9. Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimisionality: high dimensional feature and its efficient compression for face verification. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  10. Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 529–534 (2011)

    Google Scholar 

  11. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07–49, University of Massachusetts, Amherst (2007)

    Google Scholar 

  12. Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. Technical Report UM-CS-2010-009, University of Massachusetts, Amherst (2010)

    Google Scholar 

  13. Huang, G.B., Jones, M.J., Learned-Miller, E.: Lfw results using a combined nowak plus merl recognizer. In: Faces in Real-Life Images Workshop in European Conference on Computer Vision (ECCV) (2008)

    Google Scholar 

  14. Pinto, N., DiCarlo, J.J., Cox, D.D.: How far can you get with a modern face recognition test set using only simple features? In: IEEE Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  15. Simonyan, K., Parkhi, O.M., Vedaldi, A., Zisserman, A.: Fisher vector faces in the wild. In: British Machine Vision Conference (2013)

    Google Scholar 

  16. Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  17. Parkhi, O.M., Simonyan, K., Vedaldi, A., Zisserman, A.: A compact and discriminative face track descriptor. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  18. Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  19. Zhou, X., Cui, N., Li, Z., Liang, F., Huang, T.: Hierarchical gaussianization for image classification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1971–1977 (2009)

    Google Scholar 

  20. Dixit, M., Rasiwasia, N., Vasconcelos, N.: Adapted gaussian models for image classification. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 937–943 (2011)

    Google Scholar 

  21. Gross, R., Yang, J., Waibel, A.: Growing gaussian mixture models for pose invariant face recognition. In: International Conference on Pattern Recognition, vol. 1, p. 5088 (2000)

    Google Scholar 

  22. Wang, X., Tang, X.: Bayesian face recognition based on gaussian mixture models. In: 17th International Conference on Proceedings of the Pattern Recognition, ICPR 2004, vol. 4, pp. 142–145. IEEE Computer Society, Washington, DC (2004)

    Google Scholar 

  23. Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, Minneapolis, MN (2007)

    Google Scholar 

  24. Kumar, N., Berg, A., Belhumeur, P.N., Nayar, S.: Describable visual attributes for face verification and image search. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1962–1977 (2011)

    Article  Google Scholar 

  25. Wolf, L., Hassner, T., Taigman, Y.: Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1978–1990 (2011)

    Article  Google Scholar 

  26. Hua, G., Akbarzadeh, A.: A robust elastic and partial matching metric for face recognition. In: Proc. IEEE International Conference on Computer Vision, Kyoto, Japan (2009)

    Google Scholar 

  27. Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 2707–2714 (2010)

    Google Scholar 

  28. Yin, Q., Tang, X., Sun, J.: An associate-predict model for face recognition. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 497–504 (2011)

    Google Scholar 

  29. Cox, D., Pinto, N.: Beyond simple features: a large-scale feature search approach to unconstrained face recognition. In: 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), pp. 8–15 (2011)

    Google Scholar 

  30. Wang, F., Guibas, L.J.: Supervised earth mover’s distance learning and its computer vision applications. In: Proc. of European Conf. on Computer Vision, pp. 442–455 (2012)

    Google Scholar 

  31. Berg, T., Belhumeur, P.: Tom-vs-pete classifiers and identity-preserving alignment for face verification. In: Proceedings of the British Machine Vision Conference, pp. 129.1–129.11. BMVA Press (2012)

    Google Scholar 

  32. Lu, C., Tang, X.: Surpassing human-level face verification performance on lfw with gaussianface. CoRR (2014)

    Google Scholar 

  33. Wolf, L., Levy, N.: The svm-minus similarity score for video face recognition. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Porland, OR (2013)

    Google Scholar 

  34. Zhen, C., Li, W., Xu, D., Shan, S., Chen, X.: Fusing robust face region descriptors via multiple metric learning for face recognition in the wild. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ, 07030, USA

    Gang Hua

Authors
  1. Gang Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Hua .

Editor information

Editors and Affiliations

  1. Computer and Systems Engineering, Pensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

  2. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Thomas B. Moeslund

  3. Department of Computer Science, Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Department of Architecture, Design, and Media Technology, Aalborg, Denmark

    Kamal Nasrollahi

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Hua, G. (2015). Probabilistic Elastic Part Model for Real-World Face Recognition. In: Ji, Q., B. Moeslund, T., Hua, G., Nasrollahi, K. (eds) Face and Facial Expression Recognition from Real World Videos. FFER 2014. Lecture Notes in Computer Science(), vol 8912. Springer, Cham. https://doi.org/10.1007/978-3-319-13737-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-13737-7_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13736-0

  • Online ISBN: 978-3-319-13737-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation