Class-specific kernel linear regression classification for face recognition under low-resolution and illumination variation conditions

Yang Ting Chou, Shih Ming Huang, Jar Ferr Yang

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

In this paper, a novel class-specific kernel linear regression classification is proposed for face recognition under very low-resolution and severe illumination variation conditions. Since the low-resolution problem coupled with illumination variations makes ill-posed data distribution, the nonlinear projection rendered by a kernel function would enhance the modeling capability of linear regression for the ill-posed data distribution. The explicit knowledge of the nonlinear mapping function can be avoided by using the kernel trick. To reduce nonlinear redundancy, the low-rank-r approximation is suggested to make the kernel projection be feasible for classification. With the proposed class-specific kernel projection combined with linear regression classification, the class label can be determined by calculating the minimum projection error. Experiments on 8 × 8 and 8 × 6 images down-sampled from extended Yale B, FERET, and AR facial databases revealed that the proposed algorithm outperforms the state-of-the-art methods under severe illumination variation and very low-resolution conditions.

原文English
文章編號28
頁(從 - 到)1-9
頁數9
期刊Eurasip Journal on Advances in Signal Processing
2016
發行號1
DOIs
出版狀態Published - 2016 十二月 1

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 資訊系統
  • 硬體和架構
  • 電氣與電子工程

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