## MTCNN

Implementation of the MTCNN face detector for Keras in Python3.4+. It is written from scratch, using as a reference the implementation of MTCNN from David Sandberg (FaceNet’s MTCNN) in Facenet. It is based on the paper Zhang, K et al. (2016) [ZHANG2016].

## INSTALLATION

Currently it is only supported Python3.4 onwards. It can be installed through pip:

$pip install mtcnn This implementation requires OpenCV>=4.1 and Keras>=2.0.0 (any Tensorflow supported by Keras will be supported by this MTCNN package). If this is the first time you use tensorflow, you will probably need to install it in your system:$ pip install tensorflow

or with conda

\$ conda install tensorflow

Note that tensorflow-gpu version can be used instead if a GPU device is available on the system, which will speedup the results.

## USAGE

The following example illustrates the ease of use of this package:

>>> from mtcnn import MTCNN
>>> import cv2
>>>
>>> detector = MTCNN()
>>> detector.detect_faces(img)
[
{
'box': [277, 90, 48, 63],
'keypoints':
{
'nose': (303, 131),
'mouth_right': (313, 141),
'right_eye': (314, 114),
'left_eye': (291, 117),
'mouth_left': (296, 143)
},
'confidence': 0.99851983785629272
}
]

The detector returns a list of JSON objects. Each JSON object contains three main keys: ‘box’, ‘confidence’ and ‘keypoints’:

• The bounding box is formatted as [x, y, width, height] under the key ‘box’.

• The confidence is the probability for a bounding box to be matching a face.

• The keypoints are formatted into a JSON object with the keys ‘left_eye’, ‘right_eye’, ‘nose’, ‘mouth_left’, ‘mouth_right’. Each keypoint is identified by a pixel position (x, y).

Another good example of usage can be found in the file “example.py.” located in the root of this repository. Also, you can run the Jupyter Notebook “example.ipynb” for another example of usage.

### BENCHMARK

The following tables shows the benchmark of this mtcnn implementation running on an Intel i7-3612QM CPU @ 2.10GHz, with a CPU-based Tensorflow 1.4.1.

• Pictures containing a single frontal face:

Image size

Total pixels

Process time

FPS

460x259

119,140

0.118 seconds

8.5

561x561

314,721

0.227 seconds

4.5

667x1000

667,000

0.456 seconds

2.2

1920x1200

2,304,000

1.093 seconds

0.9

4799x3599

17,271,601

8.798 seconds

0.1

• Pictures containing 10 frontal faces:

Image size

Total pixels

Process time

FPS

474x224

106,176

0.185 seconds

5.4

736x348

256,128

0.290 seconds

3.4

2100x994

2,087,400

1.286 seconds

0.7

## MODEL

By default the MTCNN bundles a face detection weights model.

The model is adapted from the Facenet’s MTCNN implementation, merged in a single file located inside the folder ‘data’ relative to the module’s path. It can be overriden by injecting it into the MTCNN() constructor during instantiation.

The model must be numpy-based containing the 3 main keys “pnet”, “rnet” and “onet”, having each of them the weights of each of the layers of the network.

For more reference about the network definition, take a close look at the paper from Zhang et al. (2016) [ZHANG2016].

### REFERENCE

[ZHANG2016] (1,2)

Zhang, K., Zhang, Z., Li, Z., and Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499–1503.