A spectral feature based approach for face recognition with one training sample

Zhan Li Sun, Kin Man Lam, Zhao Yang Dong, Han Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, a novel spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm is proposed for face recognition with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Pages218-222
Number of pages5
DOIs
Publication statusPublished - 26 Nov 2012
Event2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012 - Hong Kong, Hong Kong
Duration: 12 Aug 201215 Aug 2012

Conference

Conference2012 2nd IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2012
Country/TerritoryHong Kong
CityHong Kong
Period12/08/1215/08/12

Keywords

  • classifier combination
  • Face recognition
  • Fourier transform
  • Gabor filter
  • linear discriminant analysis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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