Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Robust Real-Time Face Detection
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Automatic feature localisation with constrained local models
Pattern Recognition
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This work presents a simple and very efficient solution to align facial parts in unseen images. Our solution relies on a Point Distribution Model (PDM) face model and a set of discriminant local detectors, one for each facial landmark. The patch responses can be embedded into a Bayesian inference problem, where the posterior distribution of the global warp is inferred in a $#305;maximum a posteriori (MAP) sense. However, previous formulations do not model explicitly the covariance of the latent variables, which represents the confidence in the current solution. In our Discriminative Bayesian Active Shape Model (DBASM) formulation, the MAP global alignment is inferred by a Linear Dynamical System (LDS) that takes this information into account. The Bayesian paradigm provides an effective fitting strategy, since it combines in the same framework both the shape prior and multiple sets of patch alignment classifiers to further improve the accuracy. Extensive evaluations were performed on several datasets including the challenging Labeled Faces in the Wild (LFW). Face parts descriptors were also evaluated, including the recently proposed Minimum Output Sum of Squared Error (MOSSE) filter. The proposed Bayesian optimization strategy improves on the state-of-the-art while using the same local detectors. We also show that MOSSE filters further improve on these results.