MobileFaceNet Face Landmark Detector

author: David Wang

Description

MobileFaceNets is a class of extremely efficient CNN models to extract 68 landmarks from a facial image. It use less than 1 million parameters and is specifically tailored for high-accuracy real-time face verification on mobile and embedded devices. This repository is an adaptation from cuijian/pytorch_face_landmark.

Code Example

Extract facial landmarks from './img1.jpg'.

Write the pipeline in simplified style:

import towhee

towhee.glob('./img1.jpg') \
  .image_decode.cv2() \
  .face_landmark_detection.mobilefacenet() \
  .select('img','landmark') \
  .to_list()

Write a same pipeline with explicit inputs/outputs name specifications:

import towhee

towhee.glob['path']('./img1.jpg') \
  .image_decode.cv2['path', 'img']() \
  .face_landmark_detection.mobilefacenet['img', 'landmark']() \
  .select['img','landmark']() \
  .show()

Factory Constructor

Create the operator via the following factory method:

face_landmark_detection.mobilefacenet(pretrained = True)

Parameters:

​ pretrained

​ whether load the pre-trained weights.

​ supported types: bool, default is True, using pre-trained weights.

Interface

An image embedding operator takes an image as input. it extracts the embedding as ndarray.

Parameters:

​ img: towhee.types.Image (a sub-class of numpy.ndarray)

​ The input image.

Returns: numpy.ndarray

​ The extracted facial landmarks.