Abstract
Grassmannian manifolds have been an effective way to represent image sets (video) which are mapped as data points on the manifold. Recognition can then be performed by applying the Discriminant Analysis (DA) on such manifolds. However, the local structure of the data points are not exploited in the DA. This paper proposes a modified Kernel Discriminant Analysis (KDA) approach on Grassmannian manifolds that utilizes the local structure of the data points on the manifold. The KDA exploits the local structure using between-class and within-class adjacency graphs that represent the between-class and within-class similarities, respectively. The maximum correlation from within-class and minimum correlation from between-class is utilized to define the connectivity between points in the graph thus exploiting the geometrical structure of the data. The discriminability is further improved by effective feature representation using LBP which can discriminate data across illumination, pose, and minor expressions. Effective recognition is performed by using only the cluster representatives extracted by clustering the frames of a video sequence. Experiments on several video datasets (Honda, MoBo, ChokePoint, NRC-IIT, and MOBIO) show that the proposed approach obtains better recognition rates, in comparison with the state-of-the-art 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.
Similar content being viewed by others
References
Fukui, K., Yamaguchi, O.: Face recognition using multi-viewpoint patterns for robot vision. In: International Symposium of Robotics Research (2003)
Fan, W., Yeung, D.Y.: Locally linear models on face appearance manifolds with application to dual-subspace based classification. In: Computer Vision and Pattern Recognition (2006)
Wang, R., Shan, S., Chen, X., Gao, W.: Manifold-manifold distance with application to face recognition based on image sets. In: Computer Vision and Pattern Recognition (2008)
Lee, K.C., Mo, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Computer Vision and Pattern Recognition (2003)
Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Computer Vision and Pattern Recognition (2010)
Harandi, M.T., Sanderson, C., Shirazi, S., Lovell, B.C.: Graph embedding discriminant analysis on grassmannian manifolds for improved image set matching. In: Computer Vision and Pattern Recognition Workshop (2011)
Poh, N., Rua, E.A., Struc, V.: An evaluation of video-to-video face verification. IEEE Trans. of Information Forensics and Security 5 (2010)
Arandjelovic, O., Shakhnarovich, G., Fisher, J., Cipolla, R., Darrell, T.: Face recognition with image sets using manifold density divergence. In: Proc. of Computer Vision and Pattern Recognition, vol. 1, pp. 581–588 (2005)
Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proc. Automatic Face and Gesture Recognition, pp. 318–323 (1998)
Nishiyama, M., Yamaguchi, O., Fukui, K.: Face Recognition with the Multiple Constrained Mutual Subspace Method. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 71–80. Springer, Heidelberg (2005)
Kim, T.K., Kittler, J., Cipolla, R.: Discriminative learning and recognition of image set classes using canonical correlations. IEEE Trans. on Pattern Anal. Mach. Intell. 29, 1005–1018 (2007)
Kokiopoulou, E., Froddard, P.: Video face recognition with graph-based semi-supervised learning. In: Proc. ICME, pp. 1564–1565 (2009)
Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph enbedding and extensions: A general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51 (2007)
Mian, A.: Unsupervised learning from local features for video-based face recognition. In: IEEE Int. of Conf. Automatic Face Gesture Recognition, pp. 1–6 (2008)
Chen, J., Ye, J., Li, Q.: Integrating global and local structures: A least squares framework for dimensionality reduction. In: Proc. Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Kruger, V., Zhou, S.: Exemplar-based face recognition from video. In: IEEE Intl. on Automatic Face and Gesture Recognition, pp. 175–180 (2002)
Hadid, A., Pietikainen, M.: From still image to video-based face recognition: An experimental analysis. In: IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 813–818 (2004)
Lloyd, S.P.: Least squares quantization in pcm. IEEE Trans. on Information Theory 28, 129–137 (1982)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 2037–2041 (2006)
Hamm, J., Lee, D.D.: Grassmann discriminant analysis: A unifying view on subspace-based learning. In: Proc. Int. Conf. Machine Learning (ICML), pp. 376–383 (2008)
Hamm, J., Lee, D.D.: Extended grassmann kernels for subspace-based learning. In: Neural Information Processing Systems, pp. 601–608 (2009)
Wolf, L., Shashua, A.: Learning over sets using kernel principal angles. J. Mach. Learn. Res. 4, 913–931 (2003)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural information Processing Systems 16 (2004)
Mika, S., Ratsch, G., Weston, J., Scholkoph, B., Mullers, K.R.: Fisher discriminant analysis with kernels. Neural Networks for Signal Processing (1999)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Lee, K., Ho, J., Yang, M., Kriegman, D.: Visual tracking and recognition using probabilistic appearance manifolds. Computer Vision and Image Understanding 99, 303–331 (2005)
Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 74–81 (2011)
Gross, R., Shi, J.: The cmu motion of body (mobo) database. Technical Report CMU-RI-TR-01-18, Robotics Institute, Pittsburgh, PA (2001)
Gorodnichy, D.O.: Video-based framework for face recognition in video. In: Second Workshop on Face Processing in Video in Proceedings of Second Canadian Conference on Computer and Robot Vision, pp. 330–338.
McCool, C., Marcel, S., Hadid, A., Pietikainen, M., Matejka, P., Cernocky, J., Poh, N., Kittler, J., Larcher, A., Levy, C., Matrouf, D., Bonastre, J.F., Tresadern, P., Cootes, T.: Bi-modal person recognition on a mobile phone using mobile phone data. In: IEEE ICME Workshop on Hot Topics in Mobile Multimedia (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mahalingam, G., Kambhamettu, C. (2013). Face Recognition in Videos – A Graph Based Modified Kernel Discriminant Analysis. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)