FaceRecognition

Machine Learning project to recognise people from an Image just like facebook.

Built with the help of dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.

Dependencies:

• Python 3.x

• Numpy

• Scipy

• Scikit-learn

• dlib

  Tip: Installing dlib can be a tedious job. On macOS or Linux you may follow this link.

• Extras:

  • OpenCV (required only in webcam.py for capturing frames from the webcam)

  • For using ./demo-python-files/projecting_faces.py you will need to install Openface.

    To install Openface, follow the below instructions:

        $ git clone https://github.com/cmusatyalab/openface.git
        $ cd openface
        $ pip install -r requirements.txt
        $ sudo python setup.py install

Result:

Procedure:

• Clone this repository git clone git@github.com:anubhavshrimal/FaceRecognition.git.

Training:

• Make folder training-images.

• Add images of each person you want to recognise to a folder by their name in training-images.

  Example

  $ mkdir training-images
  $ cd training-images
  $ mkdir Name_Of_Person

  Then copy all the images of that person in ./training-images/Name_Of_Person folder.

  

• Run on cmd python create_encodings.py to get the encodings of the images and the labels. This will create encoded-images-data.csv and labels.pkl files.

  

  Note: There has to be only one face per image otherwise encoding will be for the first face found in the image.

• Run on cmd python train.py to train and save the face recognition classifier. This will create classifier.pkl file. It will also create classifier.pkl.bak backup file if the classifier with that name already exists.

  

Prediction:

• Make folder test-images which contains all the images you want to find people in.

  

• Run on cmd python predict.py to predict the faces in each image.

  

Vote of Thanks

• Thanks to Adam Geitgey whose blog inspired me to make this project.
• Thanks to Davis King for creating dlib and for providing the trained facial feature detection and face encoding models used in this project.