Face Recognition in Live Video

• Real-time video streaming with face recognition: Recognize faces in live video and serves a stream with rectangle rendering <your-host>:<5001>/stream
• Easy configuration with web interface with upload feature of known faces <your-host>:<5000>
• Eventlog with safed image if a face is recognized, viewable in web interface
• configurable UDP/HTTP Notification Service for detected faces to notify other services

• you can adjust the face recognition interval for your needs (default is every 60 frames)
• uses high performant face detection AI models
• makes extensive use of threading to use hardware resources efficiently

This guide provides instructions on how to build and run the Docker image for FaceStream.AI from the GitHub repository.

Before you begin, make sure you have the following installed:

• Git
• Docker

First, clone the FaceStream.AI repository to your local machine using the following command:

git clone https://github.com/norman-albusberger/FaceStream.AI.git

Navigate to the cloned repository directory:

cd FaceStream.AI

Build the Docker image using the following command. Replace facestream-ai with your preferred image name:

docker build -t facestream-ai .

After the image has been successfully built, you can run it with the following command. Adjust the port mappings as necessary based on the application's requirements:

docker run -p 5000:5000 -p 5001:5001 -v data:/data facestream-ai

Map the ports to your needs. The configuration data, known faces, event log and event images are stored in /data. You could map it to any volume you like.

After running the Docker image, you can verify that the web interface is up and running by accessing it through your browser:

http://localhost:5000

Wenn your input stream is reachable you can access the output stream on:

http://localhost:5001/stream

Replace localhost with your Docker host IP if necessary.

• Ensure your Docker daemon is running before executing the build and run commands.
• Modify the Dockerfile or application code as necessary for custom setups or configurations.

Contributions are welcome! Please fork the repository, make your changes, and submit a pull request.

FaceStream.AI is licensed under the GNU Affero General Public License v3.0 (AGPLv3).

• Network Use Is Distribution: Users who interact with the software over a network are afforded the same rights as those who receive binary copies. This means if you run a modified version of the software on a server and users interact with it over the network, you must also share the modified source code under AGPLv3.

• Share and Share Alike: If you distribute modified versions of the software, you must also make the source code of those versions available under the same license.

• User Protections: The license