Robust face-tracking using skin color and facial shape

Hyung Soo Lee, Daijin Kim, Sang Youn Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper proposes a robust face tracking algorithm based on the CONDENSATION algorithm that uses skin color and facial shape as observation measures. Two independent trackers are used for robust tracking: one is tracking for the skin colored region and another is tracking for the facial shape region. The two trackers are coupled by using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. The proposed face tracker shows a robust tracking performance over the skin color based tracker or the facial shape based tracker given the presence of clutter background and/or illumination changes.

Original languageEnglish
Pages (from-to)302-309
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
Publication statusPublished - 2003 Dec 1

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Face Tracking
Skin
Color
Importance sampling
Importance Sampling
Clutter
Illumination
Lighting
Face

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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AB - This paper proposes a robust face tracking algorithm based on the CONDENSATION algorithm that uses skin color and facial shape as observation measures. Two independent trackers are used for robust tracking: one is tracking for the skin colored region and another is tracking for the facial shape region. The two trackers are coupled by using an importance sampling technique, where the skin color density obtained from the skin color tracker is used as the importance function to generate samples for the shape tracker. The samples of the skin color tracker within the chosen shape region are updated with higher weights. The proposed face tracker shows a robust tracking performance over the skin color based tracker or the facial shape based tracker given the presence of clutter background and/or illumination changes.

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