A Deeply-initialized Coarse-to-fine Ensemble of Regression Trees for Face Alignment

In this paper we present DCFE, a real-time facial landmark regression method based on a coarse-to-fine Ensemble of Regression Trees (ERT). We use a simple Convolutional Neural Network (CNN) to generate probability maps of landmarks location... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Face Alignment 300W DCFE (Inter-ocular Norm) Fullset (public) 3.24 # 4
Facial Landmark Detection 300W DCFE (Inter-ocular Norm) NME 3.24 # 2
Facial Landmark Detection AFLW-Full DCFE (Box height Norm, 19 landmarks - no earlobs) Mean NME 2.17 # 3
Face Alignment COFW DCFE Mean Error Rate 5.27% # 5
Face Alignment IBUG DCFE (inter pupils normalization) Mean Error Rate 7.54% # 2

Methods used in the Paper


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