Locating Facial Landmarks Using Probabilistic Random Forest

Random forest is a useful tool for face alignment/tracking. The method of regressing local binary features learned from random forest has achieved state-of-the-art performance both in fitting accuracy and speed. Despite the great success of this method, it has certain weaknesses: the number of avail...

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Bibliographic Details
Published in:IEEE signal processing letters 2015-12, Vol.22 (12), p.2324-2328
Main Authors: Luo, Changwei, Wang, Zengfu, Wang, Shaobiao, Zhang, Juyong, Yu, Jun
Format: Article
Language:English
Subjects:
Computer vision
Conferences
Face
Face alignment
local binary features
Probabilistic logic
probabilistic random forest
probability features
Shape
Training
Vegetation
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Summary:Random forest is a useful tool for face alignment/tracking. The method of regressing local binary features learned from random forest has achieved state-of-the-art performance both in fitting accuracy and speed. Despite the great success of this method, it has certain weaknesses: the number of available local binary features is rather limited and is not optimal for face alignment; the binary features inevitably lead to serious jitter when tracking a video sequence. To address these problems, we propose learning probability features from probabilistic random forest (PRF). The proposed PRF is the same as standard random forest except that it models the probability of a sample belonging to the nodes of a tree. By using the probability features, our method significantly outperforms the state-of-the-art in terms of accuracy. It also achieves about 60 fps for locating a few facial landmarks. In addition, our method shows excellent stability in face tracking.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2015.2480758