FaceLiveSDK - Windows
Face Liveness Detection Windows Server SDK
Our SDK is fully on-premise, processing all happens on hosting server and no data leaves your server.
Installation
Prerequisites
Python 3.6+
Windows
CPU: 2 cores or more
RAM: 8GB or more
Installation Steps
Download the Face Liveness Detection Windows Server Installer
Download the Server installer for your operating system from the following link:
Install the On-premise Server
Run the installer and follow the on-screen instructions to complete the installation

Request License and Update Run MIRequest.exe file to generate a license request file. You can find it here. 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.
C:\Users\{User name}\AppData\Local\MiniAiLive\MiniAiLive-FaceLiveness-WinServer




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

API Reference
Endpoint
POST
http://127.0.0.1:8092/api/check_liveness
<Face Liveness Detection API>
Form Data:
image
: The image file (PNG, JPG, etc.) to be analyzed. This should be provided as a file upload

POST
http://127.0.0.1:8092/api/check_liveness_base64
<Face Liveness Detection API>
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

Testing API
Gradio Demo
We have included a Gradio demo to showcase the capabilities of our MiniAiLive 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
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
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 usingpip install requests
)
Example Script
This example demonstrates how to send an image file to the API endpoint and process the response.
import requestsL 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)
Face & IDSDK Online Demo, Resources
Our Products
5
1:1 & 1:N Face Matching, 2D & 3D Face Passive LivenessDetection
Request license
Feel free to Contact US to get a trial License. We are 24/7 online on WhatsApp: +19162702374.
Last updated