This is a lightweight landmarks regressor for a Smart Classroom scenario. It has a classic convolutional design: stacked 3x3 convolutions, batch normalizations, ELU 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 |
|---|---|
| RMSD (on VGGFace2) | 0.0897 |
| Face location requirements | Tight crop |
| GFlops | 0.02 |
| MParams | 0.2 |
| Source framework | Caffe* |
RMSD is a root-mean-square deviation.
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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 RGB.
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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, ..., x5, y5). All the coordinates are normalized to be in range [0,1].
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Output layer name in Inference Engine format:
landmarks_xy -
Output layer name in Inference Caffe* format:
landmarks_xy
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[*] Other names and brands may be claimed as the property of others.