Structure and appearance features for robust 3D facial actions tracking

Authors:
Stéphanie Lefèvre;Jean-Marc Odobez
Affiliations:
Idiap Research Institute, Martigny and Ecole Polytechnique Fédérale de Lausanne, Switzerland;Idiap Research Institute, Martigny and Ecole Polytechnique Fédérale de Lausanne, Switzerland
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 7
Cited 1

Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Constraints on Deformable Models with Applications to Face Tracking

International Journal of Computer Vision
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Stable Real-Time 3D Tracking Using Online and Offline Information

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active appearance models with occlusion

Image and Vision Computing
Real-time combined 2D+3D active appearance models

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On Appearance Based Face and Facial Action Tracking

IEEE Transactions on Circuits and Systems for Video Technology

Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a robust and accurate method for joint head pose and facial actions tracking, even under challenging conditions such as varying lighting, large head movements, and fast motion. This is made possible by the combination of two types of facial features. we use locations sampled from the facial texture whose appearance is initialized on the first frame and adapted over time, and also illumination-invariant patches located on characteristic points of the face such as the corners of the eyes or of the mouth. The first type of features contains rich information about the global appearance of the face and thus leads to an accurate tracking, while the second type guaranties robustness and stability by avoiding drift. We demonstrate our system on the Boston University Face Tracking benchmark, and show it outperforms state-of-the-art methods.