Active shape models—their training and application
Computer Vision and Image Understanding
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Robust Real-Time Face Detection
International Journal of Computer Vision
Automated Face Pose Estimation Using Elastic Energy Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
An active model for facial feature tracking
EURASIP Journal on Applied Signal Processing
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Overview of the Multiple Biometrics Grand Challenge
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Video Facial Feature Tracking with Enhanced ASM and Predicted Meanshift
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 02
Robust modified active shape model for automatic facial landmark annotation of frontal faces
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Video-to-video face authentication system robust to pose variations
Expert Systems with Applications: An International Journal
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In this paper we address the problem of automatically locating the facial landmarks of a single person across frames of a video sequence. We propose two methods that utilize Kalman filter based approaches to assist an Active Shape Model (ASM) in achieving this goal. The use of Kalman filtering not only aids in better initialization of the ASM by predicting landmark locations in the next frame but also helps in refining its search results and hence in producing improved fitting accuracy. We evaluate our tracking methods on frames from three video sequences and quantitatively demonstrate their reliability and accuracy.