Abstract
Video based face identification is a challenging problem as it needs to learn a robust model to account for face appearance change caused by pose, scale, expression and illumination variations. This paper proposes a novel video based face identification framework by combining fast sparse representation and incremental learning. For robust face identification, we proposed class specific subspace model based sparse representation which gives dense target coefficients and sparse error coefficients. Each subspace model is learned by using Principal Component Analysis (PCA). The test face is identified by using residual errors obtained for each model. For video based face identification, to harness the temporal information we integrated incremental face learning with our proposed face identification algorithm. By using reconstruction error and sparse error coefficients we have formulated new decision rules using rejection ratio and occlusion ratio respectively which helps in effective subspace model update in online. Numerous experiments using static and video datasets show that our method performs efficiently.
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
Chellappa, R., Ni, J., Patel, V.M.: Remote identification of faces: Problems, pros pects, and progress. International Journal of Pattern Recognition Letters 33, 1849–1859 (2012)
Barr, J.R., Bowyer, K.W., Flynn, J., Biswas, S.: Face Recognition from Video: A Review. International Journal of Pattern Recognition and Artificial Intelligence 26(5) (2012)
Turk, M., Pentland, A.: Eigen faces for face recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriengman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Recognition and Machine Intelligence 19, 711–720 (1997)
Bernd, H., Purdy, H., Tomaso, P.: Face recognition: Component based versus global approaches. Journal of Computer Vision and Image Understanding 91, 6–21 (2003)
Naseem, I., Togneri, R., Bennamoun, M.: ‘Linear Regression for Face Recognition’. IEEE Transactions on Pattern Recognition and Machine Intelligence 32(11), 2106–2112 (2010)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(2), 210–227 (2009)
Cheng, H., Liu, Z., Yang, L., Chen, X.: Sparse Representation and learning in visual recognition: Theory and Applications. International Journal on Signal Processing 93, 1408–1425 (2013)
Chen, Y.-C., Patel, V.M., Phillips, P.J., Chellappa, R.: Dictionary-based Face Recognition from Video. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 766–779. Springer, Heidelberg (2012)
Ptucha, R., Savakis, A.: Manifold based sparse representation for facial understanding in natural images. International Journal on Image and Vision Computing 31, 365–378 (2013)
Mei, X., Ling, H.: Robust visual tracking using minimization. In: Proceedings of IEEE Conference on Computer Vision, pp. 1436–1443 (2009)
Wang, D., Lu, H., Yang, M.H.: Online Object Tracking with Sparse Prototypes. IEEE Transactions on Image Processing 22(1), 314–325 (2013)
Viola, P., Michael, J.: Robust Real time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
David, A.R., Lim, J., Lin, R.S., Yang, M.H.: Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision 77(1-3), 125–141 (2008)
Chen, F., Wang, Q., Wang, S., Zhang, W., Xu, W.: ‘Object tracking via appearance modeling and sparse representation’. International Journal on Image and Vision Computing 29, 787–796 (2011)
Hale, E.T., Yin, W., Zhang, Y.: Fixed point continuation for l1 minimization: Methodology and convergence. SIAM Journal on Optimization 19(3), 1107–1130 (2008)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research 11, 19–60 (2010)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to Many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)
Lee, K.C., Ho, J., Kriegman, D.: Acquiring linear subspaces for Face Recognition under Variable Lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)
Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Visual tracking and recognition using probabilistic appearance models. International Journal of Computer Vision and Image Understanding 99(3), 303–331 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Karuppusamy, S., Jerome, J. (2014). Real-Time Video Based Face Identification Using Fast Sparse Representation and Incremental Learning. In: Thampi, S., Gelbukh, A., Mukhopadhyay, J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-04960-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-04960-1_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04959-5
Online ISBN: 978-3-319-04960-1
eBook Packages: EngineeringEngineering (R0)