Deep Convolutional Network Cascade for Facial Point DetectionDeep Convolutional Network Cascade for Facial Point DetectionSun, Yi and Wang, Xiaogang and Tang, Xiaoou2013
Paper summarycubs#### Problem addressed:
Estimation of the position of facial keypoints
#### Summary:
The authors propose an effective convolutional network cascade for facial point detection. Deep convolutional networks at the first level provide highly robust initial estimations, while shallower convolutional networks at the following two levels finely tune the initial prediction to achieve high accuracy. The method therefore can avoid local minimum caused by ambiguity and data corruption. Networks at the first level are deep convolutional networks with four convolutional stages, absolute value rectification, and locally shared weights. Networks at the second and third levels share a common shallower structure. Since they are designed to extract local features, deep structures and locally sharing weights are unnecessary.
#### Novelty:
Three-level carefully designed convolution networks
#### Drawbacks:
High computational cost because of deep model
#### Datasets:
BioID, LFPW
#### Resources:
http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
#### Presenter:
Neeti Narayan
#### Problem addressed:
Estimation of the position of facial keypoints
#### Summary:
The authors propose an effective convolutional network cascade for facial point detection. Deep convolutional networks at the first level provide highly robust initial estimations, while shallower convolutional networks at the following two levels finely tune the initial prediction to achieve high accuracy. The method therefore can avoid local minimum caused by ambiguity and data corruption. Networks at the first level are deep convolutional networks with four convolutional stages, absolute value rectification, and locally shared weights. Networks at the second and third levels share a common shallower structure. Since they are designed to extract local features, deep structures and locally sharing weights are unnecessary.
#### Novelty:
Three-level carefully designed convolution networks
#### Drawbacks:
High computational cost because of deep model
#### Datasets:
BioID, LFPW
#### Resources:
http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf
#### Presenter:
Neeti Narayan