LABELFACES: PARSING FACIAL FEATURES BY MULTICLASS LABELING WITH AN EPITOME PRIOR.
2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6.
(pp. 2453 - 2456).
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.
|Title:||LABELFACES: PARSING FACIAL FEATURES BY MULTICLASS LABELING WITH AN EPITOME PRIOR|
|Event:||16th IEEE International Conference on Image Processing|
|Dates:||2009-11-07 - 2009-11-10|
|Keywords:||Face and Gesture, Object Recognition, Markov Random Fields, Epitome|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
Archive Staff Only