Approximate Rank Order Clustering

This repository contains an implementation of this paper.

What's in this repository

clustering.py - Contains the implementaion of the clustering algorithm.

demo.py - An example to demonstrate usage. To run this, you need to download the LFW data from here. For the face vectors, I used the results from Alfred Xiang Wu's Face Verification Experiment. Also evaluates clustering on the LFW dataset using evaluation.py.

evaluation.py - Script to calculate pairwise precision and recall as explained in the paper. TODO

server.py - Script to visualize the results.

Setup

You will need cmake for this installation.

Step 1:

Create a new virtual environment and clone the repository.

mkvirtualenv (env-name)
workon (env-name)
git clone https:github.com/varun-suresh/Clustering.git

Step 2:

Follow the instructions here to install pyflann.

Step 3:

For the demo, download the LFW data and the face vectors as mentioned above and run

cd Clustering
python demo.py --lfw_path path_to_lfw_dir -v vector_file

Results

Visualization

There is a very basic visualization script in place to examine the clusters. To use the script, download the LFW images and store them in yourpath/Clustering/ directory.

Before you can run the visualization script, you must run the demo script to save the clusters. I have also uploaded the clusters file. You can download that and visualize the clusters as well.

python visualize.py --lfw_path lfw/

On your browser, open this link and you should see the clusters.

f1 score:

We get a f1 score of 0.88 ~ 0.9 on the LFW dataset.

Contributions

Thanks Mengyue for looking closely at the precision drop and correcting the error.

Timing:

Using python's multiprocessing module, clustering LFW faces took about ~40 seconds. I did this on an 8-core machine using 4 processes(Using all 8 does not improve it by much because some cores are needed for background processes). The same experiment took 7 seconds on a 20 core machine.

Citations

You should cite the following paper if you use the algorithm.

@ARTICLE{2016arXiv160400989O,
   author = {{Otto}, C. and {Wang}, D. and {Jain}, A.~K.},
    title = "{Clustering Millions of Faces by Identity}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1604.00989},

Face verification experiment

@article{wulight,
  title={A Light CNN for Deep Face Representation with Noisy Labels},
  author={Wu, Xiang and He, Ran and Sun, Zhenan and Tan, Tieniu}
  journal={arXiv preprint arXiv:1511.02683},
  year={2015}
}

If you use this implementation, please consider citing this implementation and code repository.