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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
Hadid, A., Pietikäinen, M.: Manifold learning for gender classification from face sequences. In: Proc. 3rd IAPR/IEEE International Conference on Biometrics, ICB 2009 (2009)
Seung, H.S., Lee, D.: The manifold ways of perception. Science 290(12), 2268–2269 (2000)
Kohonen, T. (ed.): Self-Organizing Maps. Springer, Berlin (1997)
Bishop, C.M., Svensen, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10(1), 215–234 (1998)
Sammon, J.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers 18(5), 401–409 (1969)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Tenenbaum, J.B., DeSilva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
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)
Sanderson, C. (ed.): Biometric Person Recognition: Face, Speech and Fusion. VDM-Verlag (2008)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)