Self-induced color correction for skin tracking under varying illumination

Abstract

An important challenge of any skin color based tracking system is to accommodate varying illumination conditions. We present a method for automatic transformation of the color planes to match the skin color model learnt for a fixed illumination. The first couple of initial frames are used to automatically extract the palm region which in turn serves as an observed skin color palette which needs to be color-transformed to match a similar palette under a known illumination. A neural network implementing back-propagation learning rule then performs the color correction for the entire sequence. We use a condensation algorithm for tracking after the color correction for improved performance.