Face Recognition using DNN

Free and open source face recognition with deep neural networks.

This Git Repository is a collection of various papers and code on the face recognition system using Python 2.7, dlib 19.4.0 and Skimage 0.9.3.

The .pdf files in this repo are some of the earliest and the fundamental papers on this topic. It also includes the Research paper from Google's FaceNet and Taigman's DeepFace.

Dependencies

• numpy
• opencv 2.4.8
• matplotlib
• dlib 19.4.0
• os
• skimage 0.9.3
• scipy 0.13.3 Use pip or easy_install to install any missing dependencies.

Usage

There are many .py files in this repo, and they are used for different purposes.

The main file is dlibopen.py which is the face recognition software. There is a folder linked with this python file and i.e images folder. The faces which needs to be recognized goes in this folder.

Once the images are there in the folder, we can start training on the persons images provided. We identify 128 points on the face which is unique to all of them, and by importing 2 files which is an opensource shape detection and face recognition model provided by dlib.

shape_predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
face_recognition_model = dlib.face_recognition_model_v1('dlib_face_recognition_resnet_model_v1.dat')

Then there are several functions which are used from drawing rectangles on the face frame... many faces can be deteceted at the same time (Also the names found are also put up on the screen to give a better UI experience), to identifying the facial landmarks within the image.

We have set the threshold to 0.6 which is the mid-way between recognizing false negatives and false positives.

Also the frames are taken in modulus 2 fashion to improve on the efficieny of running the software. (i.e every other frame is chosen). It's upto the users to change it if they have a gpu environment system.

How to run

1. Git clone or download this Repository (don't forget to STAR it!! 😀)
2. Store the images you want to recognize in the images folder. (one picture per person is enough).
3. Make sure you have all the dependencies installed.
4. Run the dlibopen.py from within the folder.
5. It should pretty much start running the software.

Here's a picture of it recognizing Chad from RHCP.