EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Generative Shape Regularization Model for Robust Face Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Multi-View Face Alignment Using 3D Shape Model for View Estimation
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Robust shape-based head tracking
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Segmentation and labeling of face images for electronic documents
Expert Systems with Applications: An International Journal
Face recognition using the POEM descriptor
Pattern Recognition
Robust face alignment based on hierarchical classifier network
ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction
Tracking facial features using mixture of point distribution models
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Sparse Representation Shape Models
Journal of Mathematical Imaging and Vision
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For multi-view face alignment, we have to deal with two major problems: 1. the problem of multi-modality caused by diverse shape variation when the view changes dramatically; 2. the varying number of feature points caused by self-occlusion. Previous works have used nonlinear models or view based methods for multi-view face alignment. However, they either assume all feature points are visible or apply a set of discrete models separately without a uniform criterion. In this paper, we propose a unified framework to solve the problem of multi-view face alignment, in which both the multi-modality and variable feature points are modeled by a Bayesian mixture model. We first develop a mixture model to describe the shape distribution and the feature point visibility, and then use an efficient EM algorithm to estimate the model parameters and the regularized shape. We use a set of experiments on several datasets to demonstrate the improvement of our method over traditional methods.