• installed dependencies tensorflow and opencv libraries.

pip install tensorflow
conda install opencv

• correct PYTHONPATH=full_path_to_folder/scr
• pretrained FaceNet model in the folder ./models/. You can download it here.
• images dataset in the ./data/folder_with_images/. I used LFW dataset. Unzip it to ./data/lfw/. The directory should be in the correct openface format:

my_database
└───a_person
│   │   image00.jpg
│   │   image01.jpg
│   
└───b_person
│   │   image00.jpg
│   
└───c_person
│   │   image00.jpg
│   │   image01.jpg
│   │   image02.jpg
│   │   image03.jpg

1. Clean folders ./np_embeddings , ./data/clustered, ./data/sorted

• Run export_embeddings.py
  This will generate embeddings and labels for images.
  To make it working add PYTHONPATH=full_path_to_folder/scr to you sources. If you are using PyCharm simply add to Enviroment Varianbles PYTHONPATH value full_path_to_folder/scr.

• Run Distance_matrix.py.
  This step will give you the matrix with all of the Euclidean distances between faces. Numpy array is saved to ./np_embeddings/embeddings.npy It will take some time because the matrix size of NxN with zeros on the main diagonal. This file is quiet big - 1.4GB. We need it to sort faces.

3. Run Cluster_faces.py.
   First, it will sort all off the face images based on the closest distance and save sorted images to ./data/sorted. Second, it will cluster images using Kmeans algorithm. Number of cluster by default is 30. You can change it if you like.

This is what you get in the end. KMeans does a pretty good job to cluster 128-dimentional image embeddings.

This work is based on FaceNet achievement. You can check FaceNet model and papers here.