Face detector for driver monitoring and similar scenarios. The network features a default MobileNet backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block.
| Metric | Value |
|---|---|
| AP (head height >10px) | 37.4% |
| AP (head height >32px) | 84.8% |
| AP (head height >64px) | 93.1% |
| AP (head height >100px) | 94.1% |
| Min head size | 90x90 pixels on 1080p |
| GFlops | 2.835 |
| MParams | 1.053 |
| Source framework | Caffe* |
Average Precision (AP) is defined as an area under the precision/recall curve. Numbers are on Wider Face validation subset.
Image, name: data, shape: 1, 3, 384, 672 in the format B, C, H, W, where:
B- batch sizeC- number of channelsH- image heightW- image width
Expected color order is BGR.
The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected
bounding boxes. The results are sorted by confidence in decreasing order. Each detection has the format
[image_id, label, conf, x_min, y_min, x_max, y_max], where:
image_id- ID of the image in the batchlabel- predicted class ID (1 - face)conf- confidence for the predicted class- (
x_min,y_min) - coordinates of the top left bounding box corner - (
x_max,y_max) - coordinates of the bottom right bounding box corner
The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:
- Face Recognition Python* Demo
- Gaze Estimation Demo
- G-API Gaze Estimation Demo
- Interactive Face Detection C++ Demo
- G-API Interactive Face Detection Demo
- Multi-Channel Face Detection C++ Demo
- Object Detection C++ Demo
- Object Detection Python* Demo
- Smart Classroom C++ Demo
- Smart Classroom C++ G-API Demo
[*] Other names and brands may be claimed as the property of others.