Overview

RTFace is a framework that selectively blurs a person's face based on his identity in real-time to protect user's privacy. It leverages object tracking to achieve real-time while running face detection using dlib, and face recognition using OpenFace.

What's in this repository?

• RTFace: RTFace module for doing real-time face denaturing.
• trainer: Trainer web server for registering faces.
• policy: Policy API server for specifying user's denaturing policy.
• broadcast: Broadcast server for viewing denatured video streams.
• camera-source: Video streaming from a webcamera
• start_demo.sh: script to start the demo.
• kill_demo.sh: script to kill the demo.
• Dockerfile: Dockerfile for building server container image
• Dockerfile-client: Dockerfile for building client container image

Server Setup

Installation

Option 1. Download pre-build RTFace container image.

docker pull jamesjue/rtface

Option 2. Clone this repo and build the image yourself.

docker build -t <image-name> .

Option 3. Installation by Hand

Install Dependencies

You should use Dockerfile as a reference.

You'll need to install following dependencies by hand as specified by its project:

• OpenFace
• Gabriel

In addition, install other dependencies as follows

sudo apt-get install redis-server && pip install -r server/requirements.txt

Run Server

If you're using docker image, use

docker run -it --rm --name <container-name> \
-p 0.0.0.0:9098:9098 -p 0.0.0.0:9111:9111 -p 0.0.0.0:10001-10004:10001-10004 \
jamesjue/rtface

If you installed everything by hand, use

./start_demo.sh

For start_demo.sh, you need to set following environment variables:

• GABRIELPATH: Path to Gabriel
• TORCHPATH: if specified, ${TORCHPATH}/bin/activate is sourced to activate torch

Here are ports the server opens:

• 10002: Trainer Web Server: Used to collect training images
• 10003: Policy API Server ("showFace" or "blurFace"): Used to convey user's privacy policy
• 10004: Broadcast Web Server: for viewing privacy-aware video stream

Client

Setup

To set up video streaming source (camera-source):

Option 1: Container

docker run --privileged --rm -it --net host \
--volume=<user home direcotry>/.Xauthority:/root/.Xauthority:rw \
--env DISPLAY=:0 --env QT_X11_NO_MITSHM=1 \
--env SERVER_IP=<your server ip> \
jamesjue/rtface-client

Option 2: Manual Installation

• Install dependency:

sudo apt-get install libopencv-dev python-opencv python-qt4

• set environment variable SERVER_IP to be the IP address of the rtface server
• Run

cd camera-source
./ui.py

How to use

The client includes a training webpage to add/delete new users, a desktop GUI as the video source stream, and a broadcast webpage showing the denatured video stream.

• To train a face to be recognized, go to https://hostname:10002. You'll need to accept self-signed certificate.
• To change a user's policy, send HTTP post form data in the following format to http://hostname:10002

http --form POST <hostname>:10003/policy uid=<email-id> policy=<"showFace" or "blurFace">

• To view video streams after privacy preservation, go to https://hostname:10004

NOTE:

1. Don't use camera-source's UI to add user, control user's policy, nor delete uesr.
2. Instead, to add user, you should use training webpage.
3. To control user's policy, use policy web api.
4. To delete a user, log in training web page and click "Clear". A user is no longer registered with the system when there are no his/her training images.
5. For the trainer web page, if you're running the server inside a container, Google Auth doesn't work due to domain name requirement. Just use email to log in.
6. The video source desktop app and the training webpage should be accessed by two different machines (e.g. desktop app on a laptop, training webpage through a smartphone), as a single camera typically cannot be accessed by two programs simultaneously.

FAQ

Tips for training a person's face

• 30 seconds of training is good enough
• please ask the user to turn the face slightly to the right, left, up, and down to capture different angles.
• RTface uses a frontal face detector, a profile face (a face that has completed turned 90 degree to the left/right) won't have too much luck to be detected.