MiniAiLive Face Liveness Detection Windows SDK

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) Windows SDK.

Note

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

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

• Python 3.6+
• Windows
• CPU: 2cores or more
• RAM: 4GB or more

1. Download the Face Liveness Detection Windows Server Installer:

   Download the Server installer for your operating system from the following link:

   Download the On-premise Server Installer

2. Install the On-premise Server:

   Run the installer and follow the on-screen instructions to complete the installation.

   

3. Request License and Update:

   Run MIRequest.exe file to generate a license request file. You can find it here.

   C:\Users\Dev-1{Your User name}\AppData\Local\MiniAiLive\MiniAiLive-FaceLiveness-WinServer

   Open it, generate a license request file, 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.

   

4. Verify Installation:

   After installation, verify that the On-premise Server is correctly installed by checking the task manager:

   

• 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

• 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--" }

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

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.

1. Install Gradio:

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

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

2. Run Gradio Demo:

   python app.py

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

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

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)

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

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.

Please visit our Face Liveness Detection API Web Demo here. https://demo.miniai.live

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.

For any inquiries or questions, please Contact US