Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model
The presence of occluders significantly impacts performance of systems
for object recognition. However, occlusion is typically treated as an
unstructured source of noise and explicit models for occluders have lagged
behind those for object appearance and shape. In this paper we describe a
hierarchical deformable part model for face detection and keypoint localization
that explicitly models occlusions of parts. The proposed model structure makes
it possible to augment positive training data with large numbers of
synthetically occluded instances. This allows us to easily incorporate the
statistics of occlusion patterns in a discriminatively trained model. We
test the model on several benchmarks for keypoint localization including
challenging sets featuring significant occlusion. We find that the addition of
an explicit model of occlusion yields a system that outperforms existing
approaches in keypoint localization accuracy.
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Text Reference
Golnaz Ghiasi and Charless Fowlkes. Occlusion coherence: localizing occluded faces with a hierarchical deformable part model. In CVPR. 2014.BibTeX Reference
@inproceedings{GhiasiF_CVPR_2014,AUTHOR = "Ghiasi, Golnaz and Fowlkes, Charless",
TITLE = "Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model",
BOOKTITLE = "CVPR",
YEAR = "2014",
tag = "object_recognition,people"
}