On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A Sparse Probabilistic Learning Algorithm for Real-Time Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Active Appearance Models Revisited
International Journal of Computer Vision
Accurate 3D Tracking of Rigid Objects with Occlusion Using Active Appearance Models
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Evaluating Error Functions for Robust Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Fast Active Appearance Model Search Using Canonical Correlation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Error Bound Minimisation for AAM Alignment
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Generic vs. person specific active appearance models
Image and Vision Computing
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Mapping from speech to images using continuous state space models
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive active appearance models
IEEE Transactions on Image Processing
Facial feature extraction on fiducial points and used in face recognition
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Facial expression based automatic album creation
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Regression based automatic face annotation for deformable model building
Pattern Recognition
Learning 3d AAM fitting with kernel methods
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Depression analysis: a multimodal approach
Proceedings of the 14th ACM international conference on Multimodal interaction
Continuous regression for non-rigid image alignment
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Generic active appearance models revisited
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Diagnosis of depression by behavioural signals: a multimodal approach
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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The active appearance model (AAM) is a powerful method for modeling and segmenting deformable visual objects. The utility of the AAM stems from two fronts: its compact representation as a linear object class and its rapid fitting procedure, which utilizes fixed linear updates. Although the original fitting procedure works well for objects with restricted variability when initialization is close to the optimum, its efficacy deteriorates in more general settings, with regards to both accuracy and capture range. In this paper, we propose a novel fitting procedure where training is coupled with, and directly addresses, AAM fitting in its deployment. This is achieved by simulating the conditions of real fitting problems and learning the best set of fixed linear mappings, such that performance over these simulations is optimized. The power of the approach does not stem from an update model with larger capacity, but from addressing the whole fitting procedure simultaneously. To motivate the approach, it is compared with a number of existing AAM fitting procedures on two publicly available face databases. It is shown that this method exhibits convergence rates, capture range and convergence accuracy that are significantly better than other linear methods and comparable to a nonlinear method, whilst affording superior computational efficiency.