mtcnn (composite)

Use Case and High-Level Description

The composite mtcnn model is mtcnn group of models designed to perform face detection. Short for “Multi-task Cascaded Convolutional Neural Network”, it is implemented using the Caffe* framework. For details about this family of models, check out the repository.

The input for each models is a blob with specific face data. The mean values need to be subtracted as follows: [127.5, 127.5, 127.5] before passing the image blob into the network. In addition, values must be divided by 128.

Composite model specification

Metric	Value
Type	Detection
Source framework	Caffe*

Accuracy

Metric	Value
mAP	48.1308%
Recall	62.2625%

mtcnn-p model specification

The “p” designation indicates that the mtcnn-p model is the “proposal” network intended to find the initial set of faces.

The model input is an image containing the data to be analyzed.

The model output is a blob with a vector containing the first pass of face data. If there are no faces detected, no further processing is needed. Otherwise, you will typically use this output as input to the mtcnn-r model.

Metric	Value
GFLOPs	3.366
MParams	0.007

Input

Original model

Image, shape - 1, 3, 720, 1280, format is B, C, W, H, where:

• B - batch size

• C - channel

• W - width

• H - height

Expected color order: RGB. Mean values - [127.5, 127.5, 127.5], scale value - 128

Converted model

Image, shape - 1, 3, 720, 1280, format is B, C, W, H, where:

• B - batch size

• C - channel

• W - width

• H - height

Expected color order: RGB.

Output

Original model

1. Face detection, name - prob1, shape - 1, 2, W, H, contains scores across two classes (0 - no face, 1 - face) for each pixel whether it contains face or not.

2. Face location, name - conv4-2, contains regions with detected faces.

Converted model

1. Face detection, name - prob1, shape - 1, 2, W, H, contains scores across two classes (0 - no face, 1 - face) for each pixel whether it contains face or not.

2. Face location, name - conv4-2, contains regions with detected faces.

mtcnn-r model specification

The “r” designation indicates that the mtcnn-r model is the “refine” network intended to refine the data returned as output from the “proposal” mtcnn-p network.

The model input is a blob with a vector containing the first pass of face data, as returned by the mtcnn-p model.

The model output is a blob with a vector containing the refined face data. If there are no faces detected by the refine pass, no further processing is needed. Otherwise, you will typically use this output as input to the mtcnn-o model.

Metric	Value
GFLOPs	0.003
MParams	0.1

Input

Original model

Image, name - data, shape - 1, 3, 24, 24 in B, C, W, H format, where:

• B - input batch size

• C - number of image channels

• W - width

• H - height

Expected color order: RGB Mean values - [127.5, 127.5, 127.5], scale value - 128

Converted model

Image, name - data, shape - 1, 3, 24, 24 in B, C, W, H format, where:

• B - input batch size

• C - number of image channels

• W - width

• H - height

Expected color order: RGB

Output

Original model

1. Face detection, name - prob1, shape - 1, 2, B, contains scores across two classes (0- no face, 1 - face) for each input in batch. This is necessary to refine face regions from mtcnn-p.

2. Face location, name - conv5-2, contains clarifications for boxes produced by mtcnn-p.

Converted model

1. Face detection, name - prob1, shape - 1, 2, B, contains scores across two classes (0- no face, 1 - face) for each input in batch. This is necessary to refine face regions from mtcnn-p.

2. Face location, name - conv5-2, contains clarifications for boxes produced by mtcnn-p.

mtcnn-o model specification

The “o” designation indicates that the mtcnn-o model is the “output” network intended to take the data returned from the “refine” mtcnn-r network, and transform it into the final output data.

The model input is a blob with a vector containing the refined face data, as returned by the mtcnn-r model.

The model output is a blob with a vector containing the output face data.

Metric	Value
GFLOPs	0.026
MParams	0.389

Input

Original model

Image, name - data, shape - 1, 3, 48, 48 in B, C, W, H format, where:

• B - input batch size

• C - number of image channels

• W - width

• H - height

Expected color order: RGB. Mean values - [127.5, 127.5, 127.5], scale value - 128

Converted model

Image, name - data, shape - 1, 3, 48, 48 in B, C, W, H format, where:

• B - input batch size

• C - number of image channels

• W - width

• H - height

Expected color order: RGB.

Output

Original model

1. Face detection, name - prob1, shape - 1, 2, B, contains scores across two classes (0- no face, 1 - face) for each input in batch. This is necessary for final face regions refining aftermtcnn-p and mtcnn-r.

2. Face location, name - conv6-2, contains final clarifications for boxes produced by mtcnn-p and refined by mtcnn-r.

3. Control points, name - conv6-3, contains five facial landmarks: left eye, right eye, nose, left mouth corner, right mouth corner coordinates for each face region.

Converted model

1. Face detection, name - prob1, shape - 1, 2, B, contains scores across two classes (0- no face, 1 - face) for each input in batch. This is necessary for final face regions refining aftermtcnn-p and mtcnn-r.

2. Face location, name - conv6-2, contains final clarifications for boxes produced by mtcnn-p and refined by mtcnn-r.

3. Control points, name - conv6-3, contains five facial landmarks: left eye, right eye, nose, left mouth corner, right mouth corner coordinates for each face region.

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities:

• G-API Face Detection MTCNN Demo

• Face Detection MTCNN Python* Demo

Legal Information

The original model is distributed under the following license:

MIT License

Copyright (c) 2016 Kaipeng Zhang

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