A Bayesian Approach to Deformed Pattern Matching of Iris Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D vs. 3D Deformable Face Models: Representational Power, Construction, and Real-Time Fitting
International Journal of Computer Vision
Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models
International Journal of Computer Vision
Adaptive active appearance model with incremental learning
Pattern Recognition Letters
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evaluation of video-to-video face verification
IEEE Transactions on Information Forensics and Security
Face tracking with automatic model construction
Image and Vision Computing
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
Deformable object modelling and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Robust active shape model construction and fitting for facial feature localization
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
| Hi-index | 0.00 |
Active Appearance Models (AAMs) are generative parametric models that have been successfully used in the past to track faces in video. A variety of video applications are possible, including dynamic pose estimation for real-time user interfaces, lip-reading, and expression recognition. To construct an AAM, a number of training images of faces with a mesh of canonical feature points (usually hand-marked) are needed. All feature points have to be visible in all training images. However, in many scenarios parts of the face may be occluded. Perhaps the most common cause of occlusion is 3D pose variation, which can cause self-occlusion of the face. Furthermore, tracking using standard AAM fitting algorithms often fails in the presence of even small occlusions. In this paper we propose algorithms to construct AAMs from occluded training images and to efficiently track faces in videos containing occlusion. We evaluate our algorithms both quantitatively and qualitatively and show successful real-time face tracking on a number of image sequences containing varying degrees of occlusions.