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Multimedia Tools and Applications

, Volume 77, Issue 24, pp 32243–32256 | Cite as

Discriminative face recognition via kernel sparse representation

  • Keyou Zhang
  • Yali Peng
  • Shigang Liu
Article

Abstract

Sparse representation (SR) is a popular method in pattern recognition and computer vision, and achieves the noticeable performance for face recognition (FR) task. Nevertheless, the conventional SR algorithm is usually computationally expensive due to the solution of representation coefficients via l1-regularization minimization problem. Besides, the internal relationship of data, such as nonlinear structure is neglected by the classification procedure conducted on the original data space. To solve these problems, this paper proposes a discriminative FR method using kernel sparse representation (KSR) based on the framework of l2-regularization. With the goal of extracting richer information, a kernel function is used to map the original face samples into a high feature space. Then, a new SR method based on the framework of l2-regularization is designed to represent the face samples on this new space. This method can produce a discriminative representation for each face sample. In addition, the proposed method offers a computational efficient algorithm for FR task. Extensive experiments conducted on the face databases show the effectiveness of our method.

Keywords

Face recognition Sparse representation l2-regularization Kernel trick 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61672333, 61402274, 61703096), the Pivot Science and Technology Innovation Team of Shaanxi Province of China (No.2014KTC-18), the Key Science and Technology Program of Shaanxi Province of China (No.2016GY-081), the Science Research and Development Program of Shaanxi Province (No.2016NY-176), Fundamental Research Funds for the Central Universities (No.GK201803059, GK201803088), China Postdoctoral Science Foundation(No.2017 M611655), Industry university cooperative education project of Higher Education Department of the Ministry of Education (No.201701023062) and Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Modern Teaching TechnologyMinistry of EducationXi’anChina
  2. 2.Engineering Laboratory of Teaching Information Technology of Shaanxi ProvinceXi’anChina
  3. 3.School of Computer ScienceShaanxi Normal UniversityXi’anChina

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