Adjusted pixel features for robust facial component classification

  • Authors:

  • Christoph Mayer;Matthias Wimmer;Bernd Radig

  • Affiliations:

  • Technische Universität München, Informatix IX, Boltzmannstraíe 1, 85748 Garching, Germany;Technische Universität München, Informatix IX, Boltzmannstraíe 1, 85748 Garching, Germany;Technische Universität München, Informatix IX, Boltzmannstraíe 1, 85748 Garching, Germany

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Within the last decade increasing computing power and the scientific advancement of algorithms allowed the analysis of various aspects of human faces such as facial expression estimation [20], head pose estimation [17], person identification [2] or face model fitting [31]. Today, computer scientists can use a bunch of different techniques to approach this challenge [4,29,3,17,9,21]. However, each of them still has to deal with non-perfect accuracy or high execution times. This is mainly because the extraction of descriptive features is challenging in real-world scenarios to any image understanding application. In this paper, we consider the extraction of more descriptive information from the image for face analysis tasks. Our approach automatically determines a set of characteristics that describe the conditions of the entire image. They are based on semantic information that describes the location of facial components, such as skin, lips, eyes, and brows. From these image characteristics, pixel features are determined that are highly tuned to the task of interpreting images of human faces. The extracted features are applied to train pixel-based classifiers, which is the straight-forward approach because this task suffers from high intra-class and small inter-class color variations due to changing context conditions such as the person's ethnic group or lighting condition. In contrast, more elaborate classifiers that additionally consider shape or region features are not real-time capable. The success of this approach relies on the fact that we do not manually select the calculation rules but we provide a multitude of features of various kinds, both color-related and space-related. A Machine Learning algorithm then decides which of them are important and which are not rendering the approach fast due to its pixel-based nature and accurate due to the highly descriptive features the same time.