Labelfaces: parsing facial features by multiclass labeling with an epitome prior

Authors:
Jonathan Warrell;Simon J. D. Prince
Affiliations:
University College London, Department of Computer Science, London, UK;University College London, Department of Computer Science, London, UK
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 0

Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Belief Networks and Neural Networks for Scene Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Epitomic analysis of appearance and shape

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Multiscale conditional random fields for image labeling

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of parsing facial features from an image labeling perspective. We learn a per-pixel unary classifier, and a prior over expected label configurations, allowing us to estimate a dense labeling of facial images by part (e.g. hair, mouth, moustache, hat). This approach deals naturally with large variations in shape and appearance characteristic of unconstrained facial images, and also the problem of detecting classes that may be present or absent. We use an Adaboost-based unary classifier, and develop a family of priors based on 'epitomes' which are shown to be particularly effective in capturing the non-stationary aspects of face label distributions.