Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning

Authors

Yeqin Cui, College of Mathematics and Information Science, Langfang Normal University, Langfang 065000, China;
Jianguo Gao, College of Mathematics and Information Science, Langfang Normal University, Langfang 065000, China;

Abstract

Abstract: Existing subspace learning-based face recognition methods assume the same loss from all misclassifications. In the real-world face recognition applications, however, different misclassifications can lead to different losses. Motivated by this concern, a cost-sensitive supervised manifold learning approach for face recognition was proposed. The proposed approach incorporated a cost matrix to specify the different costs associated with misclassifications of subjects, into locality preserving projection algorithm, which devised the corresponding cost-sensitive methods, namely, cost-sensitive locality preserving projections (Cos-Sen LPP), to achieve a minimal overall loss. Three face databases were put into the experiments and experimental results show that Cos-Sen LPP method can achieve minimal cost than existing subspace learning-based face recognition methods.

Recommended Citation

Cui, Yeqin and Gao, Jianguo (2020) "Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning," Journal of System Simulation: Vol. 28: Iss. 5, Article 11.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/11

First Page

1077

Revised Date

2015-05-08

DOI Link

https://doi.org/

Last Page

1085

CLC

TP391

Recommended Citation

Cui Yeqin, Gao Jianguo. Face Recognition Method Based on Cost-Sensitive Supervised Manifold Learning[J]. Journal of System Simulation, 2016, 28(5): 1077-1085.