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.



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.


Image, name: data, shape: 1, 3, 48, 48 in the format B, C, H, W, where:

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

The expected color order is BGR.


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].

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