Image chromatic adaptation using ANNs for skin color adaptation

P. Kakumanu, S. Makrogiannis, R. Bryll, Sethuraman Panchanathan, N. Bourbakis

Research output: Contribution to journalConference article

16 Scopus citations

Abstract

The goal of image chromatic adaptation is to remove the effect of illumination and to obtain color data that reflects precisely the physical contents of the scene. We present in this paper an approach to image chromatic adaptation using neural networks (NN) with application for detecting adapting human skin color. The network is trained on randomly chosen color images containing human subject under various illuminating conditions, thereby enabling the model to dynamically adapt to the changing illumination conditions. The proposed network predicts directly the illuminant estimate in the image so as to adapt to the human skin color. The comparison of our method with Gray World, White Patch and Neural Network on White Patch algorithms is presented. We also present our results on detecting skin regions in NN color corrected test images. The results are promising and suggest a new approach for adapting human skin color using NN's.

Original languageEnglish (US)
Pages (from-to)478-485
Number of pages8
JournalProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
StatePublished - Dec 1 2004
EventProceedings - 16th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2004 - Boca Raton, FL, United States
Duration: Nov 15 2004Nov 17 2004

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Keywords

  • CMCCAT2000
  • Face Detection
  • Image Chromatic Adaptation
  • Neural Networks
  • Skin Color Adaptation

ASJC Scopus subject areas

  • Engineering(all)

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