landmarks-regression-retail-0009

Use Case and High-Level Description

This is a lightweight landmarks regressor for the Smart Classroom scenario. It has a classic convolutional design: stacked 3x3 convolutions, batch normalizations, PReLU activations, and poolings. Final regression is done by the global depthwise pooling head and FullyConnected layers. The model predicts five facial landmarks: two eyes, nose, and two lip corners.

Example

Specification

Metric Value
Mean Normed Error (on VGGFace2) 0.0705
Face location requirements Tight crop
GFlops 0.021
MParams 0.191
Source framework PyTorch*

Normed Error (NE) for ith sample has the following form:

where N is the number of landmarks, p-hat and p are, correspondingly, the prediction and ground truth vectors of kth landmark of ith sample, and di is the interocular distance for ith sample.

Inputs

Name: "data" , shape: [1x3x48x48] - An input image in the format [BxCxHxW], where:

  • B - batch size
  • C - number of channels
  • H - image height
  • W - image width

The expected color order is BGR.

Outputs

  1. The net outputs a blob with the shape: [1, 10], containing a row-vector of 10 floating point values for five landmarks coordinates in the form (x0, y0, x1, y1, ..., x4, y4). All the coordinates are normalized to be in range [0,1].

Legal Information

[*] Other names and brands may be claimed as the property of others.