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Manifold Learning for Video-to-Video Face Recognition

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Biometric ID Management and Multimodal Communication (BioID 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5707))

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Abstract

We look in this work at the problem of video-based face recognition in which both training and test sets are video sequences, and propose a novel approach based on manifold learning. The idea consists of first learning the intrinsic personal characteristics of each subject from the training video sequences by discovering the hidden low-dimensional nonlinear manifold of each individual. Then, a target face video sequence is projected and compared to the manifold of each subject. The closest manifold, in terms of a recently introduced manifold distance measure, determines the identity of the person in the sequence. Experiments on a large set of talking faces under different image resolutions show very promising results (recognition rate of 99.8%), outperforming many traditional approaches.

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References

  1. Aggarwal, G., Chowdhury, A.R., Chellappa, R.: A system identification approach for video-based face recognition. In: 17th International Conference on Pattern Recognition, August 2004, vol. 4, pp. 175–178 (2004)

    Google Scholar 

  2. Liu, X., Chen, T.: Video-based face recognition using adaptive hidden markov models. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, June 2003, pp. 340–345 (2003)

    Google Scholar 

  3. Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, June 2003, pp. 313–320 (2003)

    Google Scholar 

  4. Hadid, A., Pietikäinen, M.: Selecting models from videos for appearance-based face recognition. In: 17th International Conference on Pattern Recognition, August 2004, vol. 1, pp. 304–308 (2004)

    Google Scholar 

  5. Heisele, B., Ho, P., Wu, J., Poggio, T.: Face recognition: Component based versus global approaches. Computer Vision and Image Understanding 91(1-2), 6–21 (2003)

    Article  Google Scholar 

  6. Hadid, A., Pietikäinen, M.: An experimental investigation about the integration of facial dynamics in video-based face recognition. Electronic Letters on Computer Vision and Image Analysis (ELCVIA) 5(1), 1–13 (2005)

    Google Scholar 

  7. Hadid, A., Pietikäinen, M.: Manifold learning for gender classification from face sequences. In: Proc. 3rd IAPR/IEEE International Conference on Biometrics, ICB 2009 (2009)

    Google Scholar 

  8. Seung, H.S., Lee, D.: The manifold ways of perception. Science 290(12), 2268–2269 (2000)

    Article  Google Scholar 

  9. Kohonen, T. (ed.): Self-Organizing Maps. Springer, Berlin (1997)

    MATH  Google Scholar 

  10. Bishop, C.M., Svensen, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10(1), 215–234 (1998)

    Article  MATH  Google Scholar 

  11. Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)

    Article  Google Scholar 

  12. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  13. Tenenbaum, J.B., DeSilva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  14. Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems 14, pp. 585–591. MIT Press, Cambridge (2002)

    Google Scholar 

  15. Sanderson, C. (ed.): Biometric Person Recognition: Face, Speech and Fusion. VDM-Verlag (2008)

    Google Scholar 

  16. Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Machine Vision Group, University of Oulu, P.O. Box 4500, 90014, FI, Finland

    Abdenour Hadid & Matti Pietikäinen

Authors
  1. Abdenour Hadid
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  2. Matti Pietikäinen
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Editor information

Editors and Affiliations

  1. Escuela Politecnica Superior, Universidad Autonoma de Madrid, C/ Francisco Tomas y Valiente 11, 28049, Madrid, Spain

    Julian Fierrez  & Javier Ortega-Garcia  & 

  2. Second University of Naples, and IIASS, Via Vivaldi 43, 81100, Caserta, Italy

    Anna Esposito

  3. EPFL, Speech Processing and Biometrics Group, EPFL-STI-IEL-LIDIAP, ELE 233, Station 11, 1015, Lausanne, Switzerland

    Andrzej Drygajlo

  4. Escola Universitària Politècnica de Mataró, Avda. Puig i Cadafalch 101-111, 08303, Mataro (Barcelona), Spain

    Marcos Faundez-Zanuy

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© 2009 Springer-Verlag Berlin Heidelberg

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Hadid, A., Pietikäinen, M. (2009). Manifold Learning for Video-to-Video Face Recognition. In: Fierrez, J., Ortega-Garcia, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds) Biometric ID Management and Multimodal Communication. BioID 2009. Lecture Notes in Computer Science, vol 5707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04391-8_2

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  • DOI: https://doi.org/10.1007/978-3-642-04391-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04390-1

  • Online ISBN: 978-3-642-04391-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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