Increasing the Robustness of 2D Active Appearance Models for Real-World Applications

Authors:
Ronny Stricker;Christian Martin;Horst-Michael Gross
Affiliations:
Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Germany;Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Germany and MetraLabs GmbH, Germany;Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Germany
Venue:
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Year:
2009

Citing 7
Cited 0

Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Lucas-Kanade 20 Years On: A Unifying Framework

International Journal of Computer Vision
Cascaded Classification of Gender and Facial Expression using Active Appearance Models

FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Emotion recognition using facial expressions with active appearance models

HCI '08 Proceedings of the Third IASTED International Conference on Human Computer Interaction
Face recognition under varying lighting conditions using self quotient image

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Properties and performance of a center/surround retinex

IEEE Transactions on Image Processing
A multiscale retinex for bridging the gap between color images and the human observation of scenes

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach to increase the robustness of Active Appearance Models (AAMs) within the scope of human-robot-interaction. Due to unknown environments with changing illumination conditions and different users, which may perform unpredictable head movements, standard AAMs suffer from a lack of robustness. Therefore, this paper introduces several methods to increase the robustness of AAMs. In detail, we optimize the shape model to certain applications by using genetic algorithms. Furthermore, a modified retinex-filter to reduce the influence of illumination is presented. These approaches are finally combined with an adaptive parameter fitting approach, which can handle bad initializations. We obtain very promising results of experiments evaluating the IMM face database [1].