Precise eye detection using discriminating HOG features

Shuo Chen, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


We present in this paper a precise eye detection method using Discriminating Histograms of Oriented Gradients (DHOG) features. The DHOG feature extraction starts with a Principal Component Analysis (PCA) followed by a whitening transformation on the standard HOG feature space. A discriminant analysis is then performed on the reduced feature space. A set of basis vectors, based on the novel definition of the within-class and between-class scatter vectors and a new criterion vector, is defined through this analysis. The DHOG features are derived in the subspace spanned by these basis vectors. Experiments on Face Recognition Grand Challenge (FRGC) show that (i) DHOG features enhance the discriminating power of HOG features and (ii) our eye detection method outperforms existing methods.

Original languageEnglish (US)
Title of host publicationComputer Analysis of Images and Patterns - 14th International Conference, CAIP 2011, Proceedings
Number of pages8
EditionPART 1
StatePublished - 2011
Event14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011 - Seville, Spain
Duration: Aug 29 2011Aug 31 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6854 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Computer Analysis of Images and Patterns, CAIP 2011

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Discriminant Analysis
  • Eye Detection
  • Face Recognition Grand Challenge (FRGC)
  • Histograms of Oriented Gradients (HOG)


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