ageitgey/face detection system example.md

## face detection system example.md

      
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              face detection system example.md
            
          
    Before you start

Make sure you have python, OpenFace and dlib installed. You can either install them manually or use a preconfigured docker image that has everying already installed:
docker pull bamos/openface
docker run -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface
Pro-tip: If you are using Docker on OSX, you can make your OSX /Users/ folder visible inside a docker image like this:
docker run -v /Users:/host/Users -p 9000:9000 -p 8000:8000 -t -i bamos/openface /bin/bash
cd /root/openface

Then you can access all your OSX files inside of the docker image at /host/Users/...
ls /host/Users/

Step 1

Make a folder called ./training-images/ inside the openface folder.
mkdir training-images

Step 2

Make a subfolder for each person you want to recognize. For example:
mkdir ./training-images/will-ferrell/
mkdir ./training-images/chad-smith/
mkdir ./training-images/jimmy-fallon/

Step 3

Copy all your images of each person into the correct sub-folders. Make sure only one face appears in each image. There's no need to crop the image around the face. OpenFace will do that automatically.
Step 4

Run the openface scripts from inside the openface root directory:
First, do pose detection and alignment:
./util/align-dlib.py ./training-images/ align outerEyesAndNose ./aligned-images/ --size 96
This will create a new ./aligned-images/ subfolder with a cropped and aligned version of each of your test images.
Second, generate the representations from the aligned images:
./batch-represent/main.lua -outDir ./generated-embeddings/ -data ./aligned-images/
After you run this, the ./generated-embeddings/ sub-folder will contain a csv file with the embeddings for each image.
Third, train your face detection model:
./demos/classifier.py train ./generated-embeddings/
This will generate a new file called ./generated-embeddings/classifier.pkl. This file has the SVM model you'll use to recognize
new faces.
At this point, you should have a working face recognizer!
Step 5: Recognize faces!

Get a new picture with an unknown face. Pass it to the classifier script like this:
./demos/classifier.py infer ./generated-embeddings/classifier.pkl your_test_image.jpg
You should get a prediction that looks like this:
=== /test-images/will-ferrel-1.jpg ===
Predict will-ferrell with 0.73 confidence.

From here it's up to you to adapt the ./demos/classifier.py python script to work however you want.
Important notes:

If you get bad results, try adding a few more pictures of each person in Step 3 (especially picures in different poses).
This script will always make a prediction even if the face isn't one it knows. In a real application, you would look at the confidence score and throw away predictions with a low confidence since they are most likely wrong.