Journal
IEEE ACCESS
Volume 5, Issue -, Pages 16649-16655Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2739822
Keywords
Facial landmark localization; occlusion; SIFT; k-means clustering; latent dirichlet allocation; gibbs sampling
Categories
Funding
- National Natural Science Foundation of China [61601337]
- Excellent Dissertation Cultivation Funds of Wuhan University of Technology [2016YS069]
- Fundamental Research Funds for the Central Universities [WUT: 2017IVB025, WUT: 2017ZY083]
- National High-tech R&D Program of China (863 Program) [2015AA015403]
- Science & Technology Pillar Program of Hubei Province [2014BAA146]
- Key Natural Science Foundation of Hubei Province of China [2015CFA059, 2015CFA069]
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Recently, a local binary patterns-based initialization scheme for robust cascaded pose regression was proposed, which selects the most correlated shapes with the estimated face from training set as the initial shapes. Nevertheless, due to the massive samples in training set, the operation of selecting the most correlated shapes with the estimated shape turns out to be time-consuming. To reduce the quantity of the faces in training set for correlation analyzing in the training set, the faces should be divided into latent classes, and a face in each class could be chosen to form a smaller initial shape pool. In this paper, we view the faces and the latent classes as the latent Dirichlet allocation model. We first extract scale-invariant feature transform features from each face and employ k-means algorithm on the features to obtain a fixed number of clusters. The features in each cluster are represented by its centroid, which can be used to generate the probability distribution of a face belonging to the latent classes via Gibbs sampling. The performance of the proposed scheme is evaluated on the challenging data set of Caltech Occluded Faces in the Wild. The experimental results show that the proposed scheme can significantly reduce time cost on landmark localization by 65.84% without dropping accuracy.
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