Cost-Sensitive Sparsity Preserving Projections for Face Recognition
- DOI
- 10.2991/iccsee.2013.725How to use a DOI?
- Keywords
- cost-sensitive learning, sparse representation, cost-sensitive classifier, feature extraction, face recognition
- Abstract
As one of the most popular research topics, sparse representation (SR) technique has been successfully employed to solve face recognition task. Though current SR based methods prove to achieve high classification accuracy, they implicitly assume that the losses of all misclassifications are the same. However, in many real-world face recognition applications, this assumption may not hold as different misclassifications could lead to different losses. Driven by this concern, in this paper, we propose a cost-sensitive sparsity preserving projections (CSSPP) for face recognition. CSSPP considers the cost information of sparse representation while calculating the sparse structure of the training set. Then, CSSPP employs the sparsity preserving projection method to achieve the projection transform and keeps the sparse structure in the low-dimensional space. Experimental results on the public AR and FRGC face databases are presented to demonstrate that both of the proposed approaches can achieve high recognition rate and low misclassification loss, which validate the efficacy of the proposed approach.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaoyuan Jing AU - Wenqian Li AU - Hao Gao AU - Yongfang Yao AU - Jiangyue Man PY - 2013/03 DA - 2013/03 TI - Cost-Sensitive Sparsity Preserving Projections for Face Recognition BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 2905 EP - 2908 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.725 DO - 10.2991/iccsee.2013.725 ID - Jing2013/03 ER -