mtcnn 0.0.2

Multi-task Cascaded Convolutional Neural Networks for Face Detection, based on TensorFlow

MTCNN

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

INSTALLATION

Currently it is only supported python3 onwards. It can be installed with pip:

$ pip3 install mtcnn

USAGE

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

>>> from mtcnn.mtcnn import MTCNN
>>> import cv2
>>>
>>> img = cv2.imread("ivan.jpg")
>>> detector = MTCNN()
>>> print(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).

A good example of usage can be found in the file “example.py.” located in the root of this repository.

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.

REFERENCE

[ZHANG2016]

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.