This project uses code from https://github.com/cyh24/Joint-Bayesian. Using LBP feature has a better performance than using CNN, because we only use LFW dataset to train the Joint-Bayesian model, I think this algorithm can't get good model when use a small dataset.

We use google net to train the model, the dataset is CASIA Webface, and we use Effective Face Frontalization in Unconstrained Images to do alignment, we used PLDA to do the experiment(It's performance is better than using Joint Bayesian in this code, however the PLDA algorithm is writen using Matlab). Here is our result using BLUFR protocol:

Acc = 0.9273

@ FAR = 0.1%: VR = 68.63%.

@ FAR = 1%: VR = 83.78%.

The performance is not so good, using better dataset will be helpful, we get 97% accuracy in LFW when we use a extended CASIA dataset, but I can't get these data and models now. I will try to train new model in the future.

According to the paper "Bayesian Face Revisited: A Joint Formulation", the repository realizes the algorithm of Joint Beyesian with Python and achieve almost the same result as the paper.

1. Get the database (lbp_WDRef,id_WDRef,lbp_lfw,pairlist_lfw) Download from the Websit: http://home.ustc.edu.cn/~chendong/JointBayesian/
2. Install the numpy & scipy
3. Install the sklearn
4. If you want to use CNN to extract features, please install pycaffe and train a model
5. You need to change some path and filename in code, I think the code can explain itself.

cd src

python test_lfw.py

You can get more information in my blog If you have any question, my email: lufo816@gmail.com