On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Active shape models—their training and application
Computer Vision and Image Understanding
Pattern Recognition Letters
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Facial Feature Localization by Coupled Features
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Detection and Facial Feature Extraction in Color Image
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Eye Center Localization Using Adaptive Templates
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Facial Features Extraction in Color Images Using Enhanced Active Shape Model
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multi-template ASM Method for Feature Points Detection of Facial Image with Diverse Expressions
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A Stereo and Color-based Method for Face Pose Estimation and Facial Feature Extraction
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Probabilistic Hierarchical Face Model for Feature Localization
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Probabilistic Facial Feature Extraction Using Joint Distribution of Location and Texture Information
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Generic vs. person specific active appearance models
Image and Vision Computing
Facial component detection for efficient facial characteristic point extraction
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Automatic facial feature extraction by genetic algorithms
IEEE Transactions on Image Processing
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Facial features such as lip corners, eye corners and nose tip are critical points in a human face. Robust extraction of such facial feature locations is an important problem which is used in a wide range of applications. In this work, we propose a probabilistic framework and several methods which can extract critical points on a face using both location and texture information. The new framework enables one to learn the facial feature locations probabilistically from training data. The principle is to maximize the joint distribution of location and apperance/texture parameters. We first introduce an independence assumption which enables independent search for each feature. Then, we improve upon this model by assuming dependence of location parameters but independence of texture parameters. We model location parameters with a multi-variate Gaussian and the texture parameters are modeled with a Gaussian mixture model which are much richer as compared to the standard subspace models like principal component analysis. The location parameters are found by solving a maximum likelihood optimization problem. We show that the optimization problem can be solved using various search strategies. We introduce local gradient-based methods such as gradient ascent and Newton's method initialized from independent model locations both of which require certain non-trivial assumptions to work. We also propose a multi-candidate coordinate ascent search and a coarse-to-fine search strategy which both depend on efficiently searching among multiple candidate points. Our framework is compared in detail with the conventional statistical approaches of active shape and active appearance models. We perform extensive experiments to show that the new methods outperform the conventional approaches in facial feature extraction accuracy.