Face_Recognition_Using_OpenFace_Simplified

This is a simplified version of using openface to recognize faces.

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Libraries Requirements :-

1. OpenFace
2. Torch
3. Open CV3
4. Dlib

Models Used :-

 1. shape_predictor_68_face_landmarls.dat (OpenFace model for predicting 68 landmarks)
 2. nn4.small2.v1.t7 (Dlib model)
 3. LinearSVM (Classification Model)

Addressing Files and Directories :-

 1. './aligned-images'             ---> Contains images after aligned.
 2. './batch-represent'            ---> Contains lua script to generate 128 embeddings.
 3. './generated-embeddings'       ---> Contains 'classifier.pkl' and CSV files of 128-dimensions and labels.
 4. './models'                     --->	Contains Openface and Dlib models.
 5. './training-images'            ---> Contains training images.
 6. 'align-images.py'              ---> Python file used to align the training images.
 7. 'classifier_train.py'          ---> Python file to train a classifier and dump a 'classifier.pkl' file.
 8. 'recognition_using_webcam.py'  ---> Python file to recognize images using webcam.
 9. './samples'  		   ---> Contains sample images of the application

Steps :-

1. Clone or Download the repository.
2. Download and extract 'shape_predictor_68_face_landmarks.dat' from http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 and save it on './models' directory.
3. Download 'nn4.small2.v1.t7' from https://storage.cmusatyalab.org/openface-models/nn4.small2.v1.t7 in the './models' directory.
4. Put your training images in the './training-images' directory.
5. First align images using the command 'python align-images.py', this will create sub-directories and will store aligned images on the directory.
6. Now generate embeddings using './batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/' . Here, '-outDir' takes the directory in which the embeddings will be stored and '-data' takes the aligned-images which is our training data.
7. We will train our classifier using 'python classifier_train.py' this will store the trained classifier in the './generated_embeddings' diretory.
8. Lastly, to detect the images using webcam run 'python recognition_using_webcam.py'.