- This project uses face_recognition library to detect and recognize faces. This is the simplest face recognition library developed by Adam Geitgey.
- This library was built using dlib with deep learning. Dlib is a C++ toolkit which contains machine learning algorithms and other useful tools.
- With an accuracy of 99.38% it performs well in recognizing faces. The package also provides a command line tool which can be easily used to compare two images from a given path.
- A sample snippet of the code which uses face_recognition on two images, known.jpg and unknown.jpg:
- Here the two images “known.jpg” and “unknown.jpg” are loaded into the two respective objects. Then the face_recognition module detects faces in the image and generates face encodings.
- A face encoding is basically a way to represent the face using a set of 128 computer-generated measurements. Two different pictures of the same person would have similar encoding and two different people would have totally different encoding.
- The results can be calculated by comparing and checking the similarity of known and unknown face encoding.
- Make sure that you have python 3.6.6 installed on your local or on your virtual environment. However, a virtual environment is preferred.
- pip install cmake
- pip install dlib
- if you have encountered any error while installing dlib, install dlib via the wheel file:
pip install https://pypi.python.org/packages/da/06/bd3e241c4eb0a662914b3b4875fc52dd176a9db0d4a2c915ac2ad8800e9e/dlib-19.7.0-cp36-cp36m-win_amd64.whl#md5=b7330a5b2d46420343fbed5df69e6a3f
- pip install face_recognition
- pip install opencv-python
- First, load all your images in the "faces" directory. Make sure that all the images are of similar sizes and there's 1 image each for every person.
- Detecting faces from an image:
- Detecting faces from web cam:
- Happy coding! :)
