Two-dimensional discriminant transform for face recognition

Jian Yang, Dapeng Zhang, Xu Yong, Jing Yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

266 Citations (Scopus)

Abstract

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.
Original languageEnglish
Pages (from-to)1125-1129
Number of pages5
JournalPattern Recognition
Volume38
Issue number7
DOIs
Publication statusPublished - 1 Jul 2005

Keywords

  • Face recognition
  • Feature extraction
  • Fisher linear discriminant analysis (fld or lda)
  • Fisherfaces
  • Two-dimensional data analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this