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: 0, shape: 1, 3, 48, 48 in the format B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
The expected color order is BGR.
The net outputs a blob with the shape: 1, 10, 1, 1, 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].
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
[*] Other names and brands may be claimed as the property of others.