IDLivenessSDK - Linux

ID Document Liveness Detection Linux Server SDK

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IDLivenessSDK - Linux

ID Document Liveness Detection Linux Server SDK

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

Introduction

Welcome to the MiniAiLive ID Document Liveness Detection SDK! MiniAiLive's Complete Document Liveness Detection Solution for Digital Onboarding here! With 70% of fraud in digital onboarding and KYC happening with document fraud—or document presentation attacks—identity verification is a critical line of defense against the financial and reputational damage of document fraud. That’s where document liveness detection software comes in. It detects when an identity document is not genuine but instead a document presentation attack. With our product suite, you can address the three most common presentation attacks universally across all the common types of identity documents anywhere in the world without needing to train on document templates. Try it out today!

Installation

Prerequisites

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

Installation Steps

Download the ID Document Liveness Detection Linux 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. Go to the Download folder and run this command.

$ cd Download
$ sudo dpkg -i --force-overwrite MiniAiLive-IDLiveness-LinuxServer.deb

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

$ cd /opt/mini-idliveness/
$ sudo ./MiRequest request /home/ubuntu/Download/trial_key.miq

$ sudo ./MiRequest update /home/ubuntu/Download/trial_key.mis

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

$ systemctl list-units --state running

If you can see 'Mini-idliveness-svc.service', the server has been installed successfully. Refer the below image.

API Reference

Endpoint

POST http://127.0.0.1:8093/api/check_id_liveness <ID Document 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://127.0.0.1:8093/api/check_id_liveness_base64 <ID Document 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 ID Document 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/ID-Document-LivenessDetection-Linux-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 ID Document 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://127.0.0.1:8093/api/check_id_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)