Robust face alignment for illumination and pose invariant face recognition

Kahraman F., Kurt B., Goekmen M.

IEEE Conference on Computer Vision and Pattern Recognition, Minnesota, United States Of America, 17 - 22 June 2007, pp.3066-3067 identifier

  • Publication Type: Conference Paper / Full Text
  • City: Minnesota
  • Country: United States Of America
  • Page Numbers: pp.3066-3067
  • Istanbul Technical University Affiliated: Yes


In building a face recognition system for real-life scenarios, one usually faces the problem that is the selection of a feature-space and preprocessing methods such as alignment under varying illumination conditions and poses. In this study, we developed a robust face alignment approach based on Active Appearance Model (AAM) by inserting an illumination normalization module into the standard AAM searching procedure and inserting different poses of the same identity into the training set. The modified AAM search can now handle both illumination and pose variations in the same epoch, hence it provides better convergence in both point-to-point and point-to-curve senses. We also investigate how face recognition performance is affected by the selection of feature space as well as the proposed alignment method The experimental results show that the combined pose alignment and illumination normalization methods increase the recognition rates considerably for all feature spaces.