Heterogeneous face recognition from local structures of normalized appearance

Shengcai Liao, Dong Yi, Zhen Lei, Rui Qin, Stan Z. Li

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

107 Citations (Scopus)

Abstract

Heterogeneous face images come from different lighting conditions or different imaging devices, such as visible light (VIS) and near infrared (NIR) based. Because heterogeneous face images can have different skin spectra-optical properties, direct appearance based matching is no longer appropriate for solving the problem. Hence we need to find facial features common in heterogeneous images. For this, first we use Difference-of-Gaussian filtering to obtain a normalized appearance for all heterogeneous faces. We then apply MB-LBP, an extension of LBP operator, to encode the local image structures in the transformed domain, and further learn the most discriminant local features for recognition. Experiments show that the proposed method significantly outperforms existing ones in matching between VIS and NIR face images.

Original languageEnglish
Title of host publicationAdvances in Biometrics - Third International Conference, ICB 2009, Proceedings
Pages209-218
Number of pages10
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event3rd International Conference on Advances in Biometrics, ICB 2009 - Alghero, Italy
Duration: Jun 2 2009Jun 5 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5558 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Advances in Biometrics, ICB 2009
Country/TerritoryItaly
CityAlghero
Period6/2/096/5/09

Keywords

  • DoG
  • Face Recognition
  • Heterogeneous
  • MB-LBP

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Heterogeneous face recognition from local structures of normalized appearance'. Together they form a unique fingerprint.

Cite this