README.md

Face Liveness Detection Docker

Welcome to the MiniAiLive!

A 100% spoofing-prevention rate for both 3D printed and resin facial masks, confirms MiniAiLive® as a leading facial recognition solution for preventing biometric fraud in remote applications, such as online banking, requiring identity verification before granting access to sensitive data or valuable assets. Feel free to use our MiniAI 3D Face Passive Liveness Detection (face anti-spoofing) Docker.

Note

SDK is fully on-premise, processing all happens on hosting server and no data leaves server.

Table of Contents

• Installation Guide
• API Details
• Gradio Demo
• Python Test API Example

Face-LivenessSDK Docker Installation Guide

Prerequisites

• Python 3.6+
• Linux
• CPU: 2 cores or more
• RAM: 8 GB or more

Installation Steps

1. Download the Face Liveness Detection Docker Image:

   Download the Server Docker Image from the following link:

   Download the On-premise Server Installer

2. Install the On-premise Docker Server:

   Run the Docker Image and follow the on-screen instructions to complete the installation. Go to the Download folder and run this command.

   $ cd Download
   $ sudo docker load -i MiniAiLive-FaceLiveSDK-DockerImg.tar

You can refer our Documentation here. https://docs.miniai.live

3. Request License and Update:

   You can generate the License Request file by using this command:

   $ sudo chmod 777 ./MiRequest_FaceLiveSDK
   $ sudo ./MiRequest_FaceLiveSDK request /home/ubuntu/Download/trial_key.miq

Then you can see the license request file on your directory, and send it to us via email or WhatsApp. We will send the license based on your Unique Request file, then you can upload the license file to allow to use. Refer the below images.

$ sudo apt install chrony
$ sudo chmod 777 ./run_faceliveness_docker
$ sudo ./run_faceliveness_docker /home/ubuntu/Downloads/trial_key.mis 8092 mini-facelivesdk-server

4. Verify Installation:

   After installation, verify that the On-premise Server is correctly installed by using this command:

   $ netstat -tnpl

   If you can see opened your port correctly, the server has been installed successfully. Refer the below image.

   

Face-LivenessSDK API Details

Endpoint

• POST http://127.0.0.1:8092/api/check_liveness Face Liveness Detection API
• POST http://127.0.0.1:8092/api/check_liveness_base64 Face Liveness Detection API

Request

• URL: http://127.0.0.1:8092/api/check_liveness
• Method: POST
• Form Data:
  • image: The image file (PNG, JPG, etc.) to be analyzed. This should be provided as a file upload.

• URL: http://127.0.0.1:8092/api/check_liveness_base64
• Method: POST
• Raw Data:
  • JSON Format: { "image": "--base64 image data here--" }

Response

The API returns a JSON object with the liveness result of the input face image. Here is an example response:

Gradio Demo

We have included a Gradio demo to showcase the capabilities of our Face Liveness Detection SDK. Gradio is a Python library that allows you to quickly create user interfaces for machine learning models.

How to Run the Gradio Demo

1. Install Gradio:

   First, you need to install Gradio. You can do this using pip:

   git clone https://github.com/MiniAiLive/FaceLivenessDetection-Docker.git
   pip install -r requirement.txt
   cd gradio

2. Run Gradio Demo:

   python app.py

Python Test API Example

To help you get started with using the API, here is a comprehensive example of how to interact with the Face Liveness Detection API using Python. You can use API with another language you want to use like C++, C#, Ruby, Java, Javascript, and more

Prerequisites

• Python 3.6+
• requests library (you can install it using pip install requests)

Example Script

This example demonstrates how to send an image file to the API endpoint and process the response.

import requests

# URL of the web API endpoint
url = 'http://127.0.0.1:8092/api/check_liveness'

# Path to the image file you want to send
image_path = './test_image.jpg'

# Read the image file and send it as form data
files = {'image': open(image_path, 'rb')}

try:
    # Send POST request
    response = requests.post(url, files=files)

    # Check if the request was successful
    if response.status_code == 200:
        print('Request was successful!')
        # Parse the JSON response
        response_data = response.json()
        print('Response Data:', response_data)
    else:
        print('Request failed with status code:', response.status_code)
        print('Response content:', response.text)

except requests.exceptions.RequestException as e:
    print('An error occurred:', e)

Request license

Feel free to Contact US to get a trial License. We are 24/7 online on WhatsApp: +19162702374.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your forked repository.
5. Submit a pull request to the original repository.

Face & IDSDK Online Demo, Resources

Our Products

No	Project	Feature
1	FaceRecognition-Linux	1:1 & 1:N Face Matching
2	FaceRecognition-Windows	1:1 & 1:N Face Matching
3	FaceRecognition-Docker	1:1 & 1:N Face Matching
4	FaceRecognition-Android	1:1 & 1:N Face Matching, 2D & 3D Face Passive LivenessDetection
5	FaceRecognition-LivenessDetection-Windows	1:1 & 1:N Face Matching, 2D & 3D Face Passive LivenessDetection
6	FaceLivenessDetection-Linux	2D & 3D Face Passive LivenessDetection
7	FaceLivenessDetection-Windows	2D & 3D Face Passive LivenessDetection
8	FaceLivenessDetection-Docker	2D & 3D Face Passive LivenessDetection
9	FaceLivenessDetection-Android	2D & 3D Face Passive LivenessDetection
10	FaceMatching-Android	1:1 Face Matching
11	FaceMatching-Windows-Demo	1:1 Face Matching
12	FaceAttributes-Android	Face Attributes, Age & Gender Estimation
13	ID-DocumentRecognition-Linux	IDCard, Passport, Driver License, Credit, MRZ Recognition
14	ID-DocumentRecognition-Windows	IDCard, Passport, Driver License, Credit, MRZ Recognition
15	ID-DocumentRecognition-Docker	IDCard, Passport, Driver License, Credit, MRZ Recognition
16	ID-DocumentRecognition-Android	IDCard, Passport, Driver License, Credit, MRZ Recognition
17	ID-DocumentLivenessDetection-Linux	ID Document LivenessDetection
18	ID-DocumentLivenessDetection-Windows	ID Document LivenessDetection
19	ID-DocumentLivenessDetection-Docker	ID Document LivenessDetection

About MiniAiLive

MiniAiLive is a leading AI solutions company specializing in computer vision and machine learning technologies. We provide cutting-edge solutions for various industries, leveraging the power of AI to drive innovation and efficiency.

Contact US

For any inquiries or questions, please Contact US