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