Face recognition from visible and near-infrared images using boosted directional binary code

Linlin Shen, Jinwen He, Shipei Wu, Songhao Zheng

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

Pose and illuminations remain great challenges to current face recognition technique. In this paper, visible image (VI) and near-infrared image (NIR) are fused for performance improvement. When directional binary code is adopted as feature representation, AdaBoost algorithm and the cascade structure are used for classification. Fusion is done at decision level and classification scores are normalized using three different rules, i.e. Min-Max, Z-Score and Tanh-Estimators. Experimental results suggest that the proposed algorithm using VI achieve better performance than NIR when pose and expression variations are present. However, NIR shows much better robustness against illumination and time difference than VI. Due to the complementary information available in two image modalities, fusion of NIR and VI further improves the system performance.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers
Pages404-411
Number of pages8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 11 Aug 201114 Aug 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6839 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Intelligent Computing, ICIC 2011
Country/TerritoryChina
CityZhengzhou
Period11/08/1114/08/11

Keywords

  • Directional binary code
  • Face recognition
  • Near-infrared image

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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