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Face recognition from identity cards with OpenCV and Deep Learning.

Project description

facereg

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facereg is a module for face recognition with OpenCV and Deep Learning.

For now it can be used for just images. It is easy to use with a handy feature which downloads images from Google for you with given keywords to create dataset/s.

Uses two different technics CNN and HoG for recognition based on dlib’s face recognition system with using face_recognition. facereg has totally three different layers and only recognizer has connection on encoder.

image_layers

Prerequisites

  • CMake
  • All dependencies are listed on requirements.txt and will be installed when you install with pip.

Installation

  • Install module using pip:

    $ pip install facereg
    
  • Download the latest facereg library from: https://github.com/verifid/facereg and install module using pip:

    $ pip install -e .
    
  • Extract the source distribution and run:

    $ python setup.py build
    $ python setup.py install
    

Usage

  • google_images:
import os
from facereg import google_images

output_directory = os.getcwd() + '/datasets' # directory path where you want to save photos
image_paths = google_images.download('michael jordan', limit=3)
  • face_encoder:
import os
from facereg import face_encoder

datasets_path = os.getcwd() + '/datasets'
encodings_path = os.path.dirname(os.path.realpath(__file__)) + '/encodings.pickle'
# these are default values for this method
face_encoder.encode_faces(datasets=datasets_path, encodings=encodings_path, detection_method='cnn')
  • recognize_faces:
from facereg import recognize_faces

image_path = 'DIRECTORY PATH OF YOUR_IMAGE'
names = recognize_faces.recognize(image_path)
# returns found names from your datasets

CLI Usage

  • Download images
# -d: keyword, -l: limit
$ python -m facereg -d 'michael jordan'
$ python -m facereg -d 'michael jordan' -l 5
  • Recognition
# -i: Directory path for image
$ python -m facereg -i tests/resources/michael_jordan.jpeg

Sample Result

image_sample

Project details


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