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sudo docker pull kbyai/palmvein-recognition:latest
sudo docker run -v ./license.txt:/home/openvino/kby-ai-palmvein/license.txt -p 8081:8080 -p 9001:9000 kbyai/palmvein-recognition:latest

Palmvein-Recognition-Docker

Overview

This repository demonstrates an efficient and accurate palmvein recognition technology by implementing palm-vein comparison based on palmvein feature extraction and face matching algorithm, which was implemented via a Dockerized Flask API.
It includes features that allow for testing plamvein recognition between two images using both image files and base64-encoded images.

In this repo, we integrated KBY-AI's palmvein recognition solution into Linux Server SDK by wrapping with docker image.
We can customize the SDK to align with customer's specific requirements.

◾PalmRecognitionSDK Product List

No.	Repository	SDK Details	Status
1	Palmprint Recognition - Linux	Palmprint Comparison Linux SDK	Available
2	Palmprint Recognition - Docker	Palmprint Comparison Docker Image	Available
➡️	Palmvein Recognition - Linux	Palmvein Comparison Linux SDK	Available
4	Palmprint Recognition - Android	Palmprint Comparison Android SDK	Available
5	Palmvein Recognition - Docker	Palmvein Comparison Docker Image	Available

To get more products, please visit products here:

Try the API

Online Demo

This SDK can be tested on online test demo page here:

Postman

The API can be evaluated through Postman tool. Here are the endpoints for testing:

• Test with an image file: Send a POST request to http://127.0.0.1:8081/palmvein.
• Test with a base64-encoded image: Send a POST request to http://127.0.0.1:8081/palmvein_base64.

SDK License

This project demonstrates KBY-AI's Palmvein Recognition Server SDK, which requires a license per machine.

• The code below shows how to use the license:

  Palmvein-Recognition-Docker/app.py

  Lines 17 to 29 in c92c8d9

  	machineCode = getMachineCode()
  	print("\nmachineCode: ", machineCode.decode('utf-8'))
  	try:
  	with open(licensePath, 'r') as file:
  	license = file.read().strip()
  	except IOError as exc:
  	print("failed to open license.txt: ", exc.errno)
  	print("\nlicense: ", license)
  	ret = setActivation(license.encode('utf-8'))
  	print("\nactivation: ", ret)

• To request the license, please provide us with the machine code obtained from the getMachineCode function.

Please contact us:

🧙Email: contact@kby-ai.com
🧙Telegram: @kbyai
🧙WhatsApp: +19092802609
🧙Discord: KBY-AI
🧙Teams: KBY-AI

1. System Requirements

• CPU: 2 cores or more (Recommended: 2 cores)
• RAM: 4 GB or more (Recommended: 8 GB)
• HDD: 4 GB or more (Recommended: 8 GB)
• OS: Ubuntu 20.04 or later
• Dependency: ncnn (Version: 2024.12.26)

2. Setup and Test

• Clone the project:

  git clone https://github.com/kby-ai/Palmvein-Recognition-Docker.git

  cd Palmvein-Recognition-Docker

• Build the Docker image:

  sudo docker build --pull --rm -f Dockerfile -t kby-ai-palmvein:latest .

• Read machine code

  sudo docker run -e LICENSE="xxxxx" kby-ai-palmvein:latest
  

• Send us machine code obtained.
• Update the license.txt file by overwriting the license key that you received from KBY-AI team.
• Run the Docker container:

  sudo docker run -v ./license.txt:/home/openvino/kby-ai-palmvein/license.txt -p 8081:8080 -p 9001:9000 kby-ai-palmvein:latest

  
• Here are the endpoints to test the API through Postman: Test with an image file: Send a POST request to http://{xx.xx.xx.xx}:8081/palmvein.
  Test with a base64-encoded image: Send a POST request to http://{xx.xx.xx.xx}:8081/palmvein.
  

3. Execute the Gradio demo

• Setup Gradio Ensure that the necessary dependencies are installed.
  Gradio requires Python 3.6 or above.
  Install Gradio using pip by running the following command:

  pip install gradio

• Run the demo with the following command:

  cd gradio
  python demo.py

• SDK can be tested on the following URL: http://127.0.0.1:9000

About SDK

1. Initializing the SDK

• Obtain the machine code to activate and request a license

  machineCode = getMachineCode()
  print("\nmachineCode: ", machineCode.decode('utf-8'))

• Activate the SDK using the license key

  ret = setActivation(license.encode('utf-8'))
  print("\nactivation: ", ret)

  Once ret value is zero, SDK can get work started

2. APIs

• ROI Extraction The SDK provides a single API for detecting hands and extracting ROI from the whole hand image(palmvein).
  The function can be used as follows:

  roi, label = get_roi_image(cv2.flip(image, 1))

  • image: input image.
  • label: Left hand or Right one.
  • roi: hand ROI(Region Of Interest) image to get palm feature.
• Create Feature getFeature function returns palmvein feature against ROI data.
  

  cnt = getFeature(roi_byte, len(roi_byte), feature_array)

  • roi_byte: roi image in byte format(image should be converted to byte format by function mat_to_byets()).
  • feature_array: palmvein feature extracted from hand ROI data.
• Similiarity The getScore() function takes two palmvein features as arguments and returns score value to determine whether 2 input hands are from the same or different.

  score = getScore(feature_array1, cnt1, feature_array2, cnt2)

3. Threshold

The threshold is 0.65 as a default.

Palmvein-Recognition-Docker/app.py

Lines 109 to 110 in a3553ee

	if score >= 0.65:
	result = "Same Hand !"