Feature extraction from faces using deformable templates
International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Hierarchical Shape Modeling for Automatic Face Localization
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Automatic 3-D face model adaptation for model-based coding of videophone sequences
IEEE Transactions on Circuits and Systems for Video Technology
Bimodal HCI-related affect recognition
Proceedings of the 6th international conference on Multimodal interfaces
Face as mouse through visual face tracking
Computer Vision and Image Understanding
Automatic Fitting of a Deformable Face Mask Using a Single Image
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
SODA-boosting and its application to gender recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Personalized 3D-aided 2D facial landmark localization
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Multi-stream confidence analysis for audio-visual affect recognition
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Applying artificial neural networks for face recognition
Advances in Artificial Neural Systems
Hi-index | 0.00 |
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribution for intrinsic face configuration is defined by Active Shape Model(ASM). The likelihood model for local facial features is parameterized as Mixture of Gaussians in feature space. A hierarchical CONDENSATION framework is then proposed to estimate the face configuration parameter. In order to improve the discriminative power of likelihood distribution in feature space, a new feature subspace, Fisher Boosting feature space, is proposed and compared against PCA subspace and biased PCA subspace. Experiments show that, Fisher Boosting algorithm can generate strong classifier with less number of weaker classifiers comparing to conventional Adaboosting algorithm as illustrated in a toy problem, that the face localization with Fisher Boosting feature subspace outperforms that with PCA feature subspaces in localization accuracy and convergence rate, and that the design of hierarchical CONDENSATION framework alleviates the local minima problem which is frequently encountered by previous ASM optimization algorithms.