Validate on LFW

1. Install dependencies

In the below description it is assumed that
a) Tensorflow has been installed, and
b) the Facenet repo has been cloned, and
c) the required python modules has been installed.

2. Download the LFW dataset

1. Download unaligned images from here

In this example the archive is downloaded to ~/Downloads.

2. Extract the unaligned images to local storage

Assuming you have a directory ~/datasets for storing datasets

cd ~/datasets
mkdir -p lfw/raw
tar xvf ~/Downloads/lfw.tgz -C lfw/raw --strip-components=1

3. Set the python path

Set the environment variable PYTHONPATH to point to the src directory of the cloned repo. This is typically done something like this
export PYTHONPATH=[...]/facenet/src
where [...] should be replaced with the directory where the cloned facenet repo resides.

4. Align the LFW dataset

Alignment of the LFW dataset can be done using align_dataset_mtcnn in the align module.

Alignment of the LFW dataset is done something like this:

for N in {1..4}; do \
python src/align/align_dataset_mtcnn.py \
~/datasets/lfw/raw \
~/datasets/lfw/lfw_mtcnnpy_160 \
--image_size 160 \
--margin 32 \
--random_order \
--gpu_memory_fraction 0.25 \
& done

The parameter margin controls how much wider aligned image should be cropped compared to the bounding box given by the face detector. 32 pixels with an image size of 160 pixels corresponds to a margin of 44 pixels with an image size of 182, which is the image size that has been used for training of the model below.

5. Download pre-trained model (optional)

If you don not have your own trained model that you would like to test and easy way forward is to download a pre-trained model to run the test on. One such model can be found here. Download and extract the model and place in your favorite models directory (in this example we use ~/models/facenet/). After extracting the archive there should be a new folder 20180402-114759 with the contents

20180402-114759.pb
model-20180402-114759.ckpt-275.data-00000-of-00001
model-20180402-114759.ckpt-275.index
model-20180402-114759.meta

6. Run the test

The test is ran using validate_on_lfw:

python src/validate_on_lfw.py \
~/datasets/lfw/lfw_mtcnnpy_160 \
~/models/facenet/20180402-114759 \
--distance_metric 1 \
--use_flipped_images \
--subtract_mean \
--use_fixed_image_standardization

This will
a) load the model,
b) load and parse the text file with the image pairs,
c) calculate the embeddings for all the images (as well as their horizontally flipped versions) in the test set,
d) calculate the accuracy, validation rate (@FAR=-10e-3), the Area Under Curve (AUC) and the Equal Error Rate (EER) performance measures.

A typical output from the the test looks like this:

Model directory: /home/david/models/20180402-114759/
Metagraph file: model-20180402-114759.meta
Checkpoint file: model-20180402-114759.ckpt-275
Runnning forward pass on LFW images
........................
Accuracy: 0.99650+-0.00252
Validation rate: 0.98367+-0.00948 @ FAR=0.00100
Area Under Curve (AUC): 1.000
Equal Error Rate (EER): 0.004