Abstract
This paper proposes an Adaptive Sparse Representation pose Classification (ASRC) algorithm to deal with face pose estimation in occlusion, bad illumination and low-resolution cases. The proposed approach classifies different poses, the appearance of face images from the same pose being modelled by an online eigenspace which is built via Incremental Principal Component Analysis. Then the combination of the eigenspaces of all pose classes are used as an over-complete dictionary for sparse representation and classification. However, the big amount of training images may lead to build an extremely large dictionary which will decelerate the classification procedure. To avoid this situation, we devise a conditional update method that updates the training eigenspace only with the misclassified face images. Experimental results show that the proposed method is very robust when the illumination condition changes very dynamically and image resolutions are quite poor.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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
Huang, T., Tu, J., Xiong, Y.: Calibrating head pose estimation in vidoes for meeting room event analysis. In: IEEE International Conference on Image Processing (ICIP), pp. 3193–3196 (2006)
Murphy-Chutorian, E., Trivedi, M.: Head pose estimation for driver assistance systems: A robust algorithm and experimental evaluation. Intelligent Transportation Systems, 709–714 (2007)
Ohayon, S., Rivlin, E.: Robust 3d head tracking using camera pose estimation. In: International Conference Pattern Recognation (ICPR), pp. 1063–1066 (2006)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)
Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60(2), 135–164 (2004)
Moon, H., Miller, M.L.: Estimating facial pose from a sparse representation. In: International Conference on Image Processing (ICIP), pp. 75–78 (2004)
Yuan, J., Li, Z., Fu, Y., Huang, T.: Query driven localized linear discriminant models for head pose estimation. In: ICME, pp. 1810–1813 (2007)
Jieping, Y., Balasubramanian, V., Panchanathan, S.: Biased manifold embedding: A framework for person-independent head pose estimation. In: Proc. CVPR, pp. 1–7 (2007)
De la Torre, F., Huang, D., Storer, M., Bischof, H.: Supervised local subspace learning for continuous head pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2928 (2011)
Su, F., Su, Z., Ji, H., Liu, R., Tian, Y.: Robust head pose estimation via convex regularized sparse regression. In: IEEE International Conference on Image Processing (ICIP), pp. 3617–3620 (2011)
Saquib Sarfraz, M., Hellwich, O.: Head pose estimation in face recognition across pose scenarios. In: VISAPP, pp. 235–242 (2008)
Benfold, B., Reid, I.: Colour invariant head pose classification in low resolution video. In: Procedings of the British Machine Vision Conference, BMVC (2008)
Xiang, T., Orozco, J., Gong, S.: Head pose classification in crowded scenes. In: Procedings of the British Machine Vision Conference (BMVC 2009), pp. 120.1–120.11 (2009)
Sarfraz, M.S., Hellwich, O.: Head pose estimation in face recognition across pose scenarios. In: VISAPP (1) 2008, pp. 235–242 (2008)
Hall, P.M., Marshall, D., Martin, R.R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference, pp. 286–295 (1998)
Arvind Ganesh, S., Sastry, S., Wright, J., Yang, A.Y., Ma, Y.: Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 31(2), 210–227
Donoho, D.: For most large underdetermined systems of linear equations: the minimal l1-norm solution is also the sparsest solution. Comm. on Pure and Applied Math. 59(6), 797–829 (2006)
Baker, S., Sim, T., Bsat, M.: The cum pose illumination and expression database. IEEE Trans. Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2001)
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
Zhang, Y., Benhamza, Y., Idrissi, K., Garcia, C. (2013). Incremental Principal Component Analysis-Based Sparse Representation for Face Pose Classification. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-02895-8_56
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02894-1
Online ISBN: 978-3-319-02895-8
eBook Packages: Computer ScienceComputer Science (R0)