FaceLivenessDetection - Docker

This demo demonstrates 3D passive face liveness detection python SDK by mitigating biometric fraud on Linux server.

Overview

This project demonstrates an advanced face liveness detection technology implemented via a Dockerized Flask API.

It stands for 3D passive face liveness detection docker, face anti-spoofing docker, face fraudulent check docker, face liveness check docker, fraud prevention docker, spoof prevention docker, face fraud detection docker and biometric fraud with liveness detection on Linux server.

SDK

Face Liveness Detection SDK - Server

Github

GitHub - kby-ai/FaceLivenessDetection-Docker: This is the docker project for 3D passive face liveness detection.GitHub

dockerhub

Docker

Test Online

Gradio

Postman

To test the API, you can use Postman. Here are the endpoints for testing:

• Test with an image file: Send a POST request to http://18.221.33.238:8080/check_liveness

• Test with a base64-encoded image: Send a POST request to http://18.221.33.238:8080/check_liveness_base64

• You can download the Postman collection to easily access and use these endpoints. click here

How to Run

1. System Requirements

CPU: 2 cores or more (Recommended: 8 cores)

RAM: 4 GB or more (Recommended: 8 GB)

HDD: 4 GB or more (Recommended: 8 GB)

OS: Ubuntu 20.04 or later

Dependency: OpenVINO™ Runtime (Version: 2022.3)

2. Setup and Test

• Clone the project:

git clone https://github.com/kby-ai/FaceLivenessDetection-Windows.git

• Download the model from Google Drive and unzip it: click here

cd FaceLivenessDetection-Docker

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1bYl0p5uHXuTQoETdbRwYLpd3huOqA3wY' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1bYl0p5uHXuTQoETdbRwYLpd3huOqA3wY" -O data.zip && rm -rf /tmp/cookies.txt

unzip data.zip

• Build the Docker image:

sudo docker build --pull --rm -f Dockerfile -t kby-ai-live:latest .

• Run the Docker container:

sudo docker run -v ./license.txt:/home/openvino/kby-ai-live/license.txt -p 8080:8080 kby-ai-live

• Send us the machine code and replace the license.txt file you received. Then, run the Docker container again.

• To test the API, you can use Postman. Here are the endpoints for testing: Test with an image file: Send a POST request to http://{xx.xx.xx.xx}:8080/check_liveness. Test with a base64-encoded image: Send a POST request to http://{xx.xx.xx.xx}:8080/check_liveness_base64. You can download the Postman collection to easily access and use these endpoints. click here

3. Execute the Gradio demo

• Setup Gradio Ensure that you have the necessary dependencies installed. Gradio requires Python 3.6 or above. You can install Gradio using pip by running the following command:

pip install gradio

• Run the demo by using the following command:

cd gradio
python demo.py

• You can test within the following URL: http://127.0.0.1:9000