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Research Article | Open Access |

Competitive Representation Based Classification Using Facial Noise Detection

Author 1: Tao Liu Author 2: Cong Li Author 3: Ying Liu Author 4: Chao Li
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 3 · Published 2016

DOI: https://doi.org/10.14569/IJACSA.2016.070314

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Abstract

Linear representation based face recognition is hotly studied in recent years. Competitive representation classification is a linear representation based method which uses the most competitive training samples to sparsely represent a probe. However, possible noises on a test face image can bias the representation results. In this paper we propose a facial noise detection method to remove noises in the test image during the competitive representation. We compare the proposed method with others on AR, Extended Yale B, ORL, FERET, and LFW databases and the experimental results show the good performance of our method.

Keywords

face recognition sparse representation biometrics noise detection

How to Cite this Article

Liu, T., Li, C., Liu, Y., & Li, C. (2016). Competitive Representation Based Classification Using Facial Noise Detection. International Journal of Advanced Computer Science and Applications, 7(3). https://doi.org/10.14569/IJACSA.2016.070314

Liu, Tao, et al.. "Competitive Representation Based Classification Using Facial Noise Detection." International Journal of Advanced Computer Science and Applications, vol. 7, no. 3, 2016, https://doi.org/10.14569/IJACSA.2016.070314.

@article{Liu2016,
  title     = {Competitive Representation Based Classification Using Facial Noise Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {3},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Tao Liu and Cong Li and Ying Liu and Chao Li},
  doi       = {10.14569/IJACSA.2016.070314},
  url       = {https://doi.org/10.14569/IJACSA.2016.070314}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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