Identity recognition based on generalised linear regression classification for multi-component images

Yang Ting Chou, Jar Ferr Yang

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.

Original languageEnglish
Pages (from-to)18-27
Number of pages10
JournalIET Computer Vision
Issue number1
Publication statusPublished - 2016 Feb 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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