Kernel linear regression for low resolution face recognition under variable illumination

Shih Ming Huang, Jar-Ferr Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

To improve the limitation of linear regression classification, a class specific kernel linear regression classification is proposed for low resolution face recognition under variable illumination. The nonlinear mapping function enhances the modeling capability for highly nonlinear data distribution. The explicit knowledge of the nonlinear mapping function can be avoided computationally by using the kernel trick. With kernel projection, the class label is also determined by calculating the minimum reconstruction error. Experiments carried out on Yale B facial database in size of 8x8 pixels reveal that the proposed algorithm outperforms the state-of-the-art methods and demonstrates promising abilities against severe illumination variation.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages1945-1948
Number of pages4
DOIs
Publication statusPublished - 2012 Oct 23
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 2012 Mar 252012 Mar 30

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period12-03-2512-03-30

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Huang, S. M., & Yang, J-F. (2012). Kernel linear regression for low resolution face recognition under variable illumination. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 1945-1948). [6288286] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288286