Face recognition from identity cards with OpenCV and Deep Learning.
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.
All dependencies are listed on requirements.txt and will be installed when you install with pip.
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
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)
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')
from facereg import recognize_faces image_path = 'DIRECTORY PATH OF YOUR_IMAGE' names = recognize_faces.recognize(image_path) # returns found names from your datasets
# -d: keyword, -l: limit $ python -m facereg -d 'michael jordan' $ python -m facereg -d 'michael jordan' -l 5
# -i: Directory path for image $ python -m facereg -i tests/resources/michael_jordan.jpeg
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Hashes for facereg-0.2.1.2-py2.py3-none-any.whl