face-detection-adas-0001

Use Case and High-Level Description

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.

Example

Specification

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.

Inputs

Image, name: data, shape: 1, 3, 384, 672 in the format B, C, H, W, where:

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

Expected color order is BGR.

Outputs

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 batch
• label - 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

Demo usage

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

Legal Information

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