Abstract
Sparse representation-based classification (SRC) has been an important approach in pattern classification and widely applied to various fields of visual recognition. However, there are some practical factors to consider in real-world problems, such as pixel damage, block occlusion, illumination, and position change. In recent years, researchers have brought a large number of improvements and proposed various effective algorithms. We improve the degree of sparse representation (SR) based on the probabilistic collaborative representation framework in this paper. Furthermore, a new algorithm is proposed and successfully applied to face recognition. The effectiveness and efficiency of our method are demonstrated experimentally.
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
Similar content being viewed by others
References
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), pp. 1–8 (2008)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S, Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intel. 31(2), 210–227 (2009)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: Proceedings of 2012 IEEE International Conference on Computer Vision (ICCV 2012), pp. 471–478 (2012)
Cai, S., Zhang, L., Zuo, W., Feng, X.: A probabilistic collaborative representation based approach for pattern classification. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), pp. 2950–2959 (2016)
Peng, Y., Li, L., Liu, S., Li, J., Wang, X.: Extended sparse representation based classification method for face recognition. Mach. Vis. Appl. 1–17 (2018)
Tang, X., Feng, G., Cai, J.: Weighted group sparse representation for undersampled face recognition. Neurocomputing 145(18), 402–415 (2014)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intel. 23(6), 643–660 (2001)
Yale, B.: Face database. http://vision.ucsd.edu/content/yale-face-database
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hu, Y., Zhang, Y. (2020). A Collaborative Sparse Representation-Based Approach for Pattern Classification. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3867-4_20
Download citation
DOI: https://doi.org/10.1007/978-981-15-3867-4_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3866-7
Online ISBN: 978-981-15-3867-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)