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

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number28
Pages (from-to)1-9
Number of pages9
JournalEurasip Journal on Advances in Signal Processing
Volume2016
Issue number1
DOIs
Publication statusPublished - 2016 Dec 1

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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