Welcome to the MiniAiLive!
Check the likelihood that two faces belong to the same person. You will get a confidence score and thresholds to evaluate the similarity. Feel free to use our MiniAI Face Recognition Linux SDK.
Note
- Our SDK is fully on-premise, processing all happens on hosting server and no data leaves server.
- Python 3.6+
- Linux
- CPU: 2 cores or more
- RAM: 8 GB or more
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Download the Face Recognition Linux Server Installer:
Download the Server installer for your operating system from the following link:
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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-FaceSDK-LinuxServer.deb
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Request License and Update:
You can generate the License Request file by using this command:
$ cd /opt/miniai/face-rec-service $ sudo ./MiRequest request /home/ubuntu/Download/trial_key.miq
$ sudo ./MiRequest update /home/ubuntu/Download/trial_30.mis
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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-facesvc.service', 'Mini-fdsvc.service', the server has been installed successfully. Refer the below image.
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POST http://127.0.0.1:8083/api/face_detectFace Detection, Face Attributes API -
POST http://127.0.0.1:8083/api/face_detect_base64Face Detection, Face Attributes API -
POST http://127.0.0.1:8083/api/face_matchFace Matching API -
POST http://127.0.0.1:8083/api/face_match_base64Face Matching API
- URL:
http://127.0.0.1:8083/api/face_detect - Method:
POST - Form Data:
image: The image file (PNG, JPG, etc.) to be analyzed. This should be provided as a file upload.
- URL:
http://127.0.0.1:8083/api/face_detect_base64 - Method:
POST - Raw Data:
JSON Format: { "image": "--base64 image data here--" }
The API returns a JSON object with the recognized details from the input Face image. Here is an example response:
We have included a Gradio demo to showcase the capabilities of our Face Recognition SDK. Gradio is a Python library that allows you to quickly create user interfaces for machine learning models.
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Install Gradio:
First, you need to install Gradio. You can do this using pip:
git clone https://github.com/MiniAiLive/FaceRecognition-Linux.git pip install -r requirement.txt cd gradio -
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 Recognition 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+
requestslibrary (you can install it usingpip 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://127.0.0.1:8083/api/face_detect'
# 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. |
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| 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 |
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
