Competitive Sparse Representation Classification for Face Recognition

A method, named competitive sparse representation classification (CSRC), is proposed for face recognition in this paper. CSRC introduces a lowest competitive deletion mechanism which removes the lowest competitive sample based on the competitive ability of training samples for representing a probe in multiple rounds collaborative linear representation. In other words, in each round of competing, whether a training sample is retained or not in the next round depends on the ability of representing the input probe. Because of the number of training samples used for representing the probe decreases in CSRC, the coding vector is transformed into a low dimensional space comparing with the initial coding vector. Then the sparse representation makes CSRC discriminative for classifying the probe. In addition, due to the fast algorithm, the FR system has less computational cost. To verify the validity of CSRC, we conduct a series of experiments on AR, Extended YB, and ORL databases respectively.

@article{Liu2015,
  title     = {Competitive Sparse Representation Classification for Face Recognition},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {6},
  number    = {8},
  year      = {2015},
  publisher = {The Science and Information Organization},
  author    = {Ying Liu and Jian-Xun Mi and Cong Li and Chao Li},
  doi       = {10.14569/IJACSA.2015.060801},
  url       = {https://doi.org/10.14569/IJACSA.2015.060801}
}