KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors

Article Details
Title:	KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors
Article URLs:	https://doi.org/10.1109/FG.2017.149 http://doi.ieeecomputersociety.org/10.1109/FG.2017.149
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Authors:	Amit Kumar	University of Maryland - College Park, Department of Electrical and Computer Engineering University of Maryland - College Park, CFAR University of Maryland - College Park, UMIACS
	Azadeh Alavi	University of Maryland - College Park, Department of Electrical and Computer Engineering University of Maryland - College Park, CFAR University of Maryland - College Park, UMIACS
	Rama Chellappa	University of Maryland - College Park, Department of Electrical and Computer Engineering University of Maryland - College Park, CFAR University of Maryland - College Park, UMIACS
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DBLP Key:	conf/fgr/KumarAC17
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IEEE Face and Gesture Recognition, FG 2017