Building a Fingerprint Authentication System with Python

Introduction
Prerequisites
Setup
Steps
Conclusion

Introduction

In this tutorial, we will learn how to build a Fingerprint Authentication System using Python. Fingerprint authentication is widely used for secure access control and identification purposes. By the end of this tutorial, you will be able to capture fingerprint images, preprocess them, extract features, create a fingerprint database, and authenticate a fingerprint against the stored database.

Prerequisites

To follow along with this tutorial, you will need:

Basic understanding of Python programming language
Python installed on your machine
Familiarity with the command-line interface

Setup

Before we get started, let’s set up our development environment by following these steps:

Open your terminal or command prompt.
Create a new directory for our project: mkdir fingerprint-authentication.
Navigate to the project directory: cd fingerprint-authentication.
Create a virtual environment: python -m venv venv.
Activate the virtual environment:
- On macOS and Linux: source venv/bin/activate.
- On Windows: venv\Scripts\activate.bat.
Install the necessary libraries: pip install opencv-python numpy.

Now that our environment is set up, let’s proceed with the steps to build our fingerprint authentication system.

Steps

Step 1: Installing the Required Libraries

First, we need to install the required libraries to work with fingerprint images. We will use OpenCV for image processing and manipulation, and NumPy for numerical operations.

To install these libraries, open your terminal or command prompt and run the following command: pip install opencv-python numpy

Step 2: Capturing Fingerprint Images

The first step is to capture fingerprint images using a fingerprint scanner or a digital fingerprint sensor. You can use a pre-existing fingerprint database or capture new fingerprint images for this tutorial.

To capture fingerprint images, follow these steps:

Connect your fingerprint scanner or digital fingerprint sensor to your computer.
Open a Python file capture.py.
Import the necessary libraries:
```
 import cv2
```
Initialize the fingerprint capture device:
```
 cap = cv2.VideoCapture(0)
```

Check if the fingerprint capture device is available:

 if not cap.isOpened():
     raise IOError("Cannot open the camera")

Create a window to display the captured image:

 cv2.namedWindow("Fingerprint Capture", cv2.WINDOW_NORMAL)

Continuously capture fingerprint images:

 while True:
     ret, frame = cap.read()
	    
     # Display the captured image
     cv2.imshow("Fingerprint Capture", frame)
	    
     # Break the loop if 'q' is pressed
     if cv2.waitKey(1) & 0xFF == ord('q'):
         break

Release the capture device and close the window after capturing the required number of images:
```
 cap.release()
 cv2.destroyAllWindows()
```
Now you can run the capture.py file to start capturing fingerprint images in real-time. Press ‘q’ to stop capturing.

Step 3: Preprocessing the Images

Before extracting features from the fingerprint images, we need to preprocess the images to enhance the fingerprint patterns and remove noise.

To preprocess the images, follow these steps:

Open a Python file preprocess.py.
Import the necessary libraries:
```
 import cv2
```

Load a fingerprint image:

 image = cv2.imread("fingerprint_image.jpg", 0)

Apply image enhancement techniques:

 # Apply histogram equalization for contrast enhancement
 equ = cv2.equalizeHist(image)
	
 # Apply a blur filter to remove noise
 blur = cv2.GaussianBlur(equ, (5, 5), 0)
	
 # Apply a threshold to convert the image to binary
 _, threshold = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY_INV)

Display the preprocessed images:
```
 cv2.imshow("Original Image", image)
 cv2.imshow("Enhanced Image", equ)
 cv2.imshow("Preprocessed Image", threshold)
 cv2.waitKey(0)
 cv2.destroyAllWindows()
```
After preprocessing, you can see the original image, the enhanced image, and the preprocessed image with enhanced fingerprint patterns and reduced noise.

Step 4: Extracting Features from the Images

Next, we need to extract features from the preprocessed fingerprint images. Fingerprint features are unique characteristics that can be used for identification and comparison.

To extract features from the preprocessed images, follow these steps:

Open a Python file extract_features.py.
Import the necessary libraries:
```
 import cv2
```

Load the preprocessed image:

 image = cv2.imread("preprocessed_image.jpg", 0)

Apply feature extraction algorithms:

 # Apply feature extraction algorithm (e.g., minutiae extraction)
 features = extract_minutiae(image)

Store the extracted features in a database for future comparison and authentication.

Step 5: Creating the Fingerprint Database

Now that we have extracted features from the fingerprint images, we need to create a fingerprint database to store these features for future authentication.

To create the fingerprint database, follow these steps:

Open a Python file create_database.py.
Import the necessary libraries:
```
 import sqlite3
```

Connect to the database:

 conn = sqlite3.connect("fingerprint_database.db")
 c = conn.cursor()

Create a table to store the fingerprint features:

 c.execute("CREATE TABLE IF NOT EXISTS fingerprints (id INTEGER PRIMARY KEY, features TEXT)")

Insert the extracted features into the database:

 c.execute("INSERT INTO fingerprints (features) VALUES (?)", (features,))
 conn.commit()

Close the connection to the database:
```
 conn.close()
```
Now you have a fingerprint database ready to store and compare fingerprint features.

Step 6: Authenticating Fingerprint

Finally, we are ready to authenticate a fingerprint against the stored fingerprint database. We will compare the features extracted from the input fingerprint image with the features stored in the database.

To authenticate a fingerprint, follow these steps:

Open a Python file authenticate.py.
Import the necessary libraries:
```
 import cv2
 import sqlite3
```

Load the input fingerprint image and preprocess it:

 input_image = cv2.imread("input_image.jpg", 0)
 preprocessed_image = preprocess_image(input_image)

Extract features from the preprocessed image:

 input_features = extract_features(preprocessed_image)

Connect to the fingerprint database:

 conn = sqlite3.connect("fingerprint_database.db")
 c = conn.cursor()

Retrieve the fingerprint features from the database:

 c.execute("SELECT features FROM fingerprints")
 database_features = c.fetchall()

Compare the input features with the database features:

 for feature in database_features:
     similarity_score = compare_features(input_features, feature)
	    
     # Check if the similarity score meets the authentication threshold
     if similarity_score >= 0.8:
         print("Fingerprint authenticated successfully")
         break
     else:
         print("Fingerprint authentication failed")

Close the connection to the database:
```
 conn.close()
```
Now you can run the authenticate.py file to authenticate the input fingerprint image against the stored database.

Conclusion

In this tutorial, we have learned how to build a Fingerprint Authentication System using Python. We covered the steps to capture fingerprint images, preprocess them, extract features, create a fingerprint database, and authenticate a fingerprint against the stored database. By applying these techniques, you can create your own fingerprint authentication system for secure access control and identification purposes.

Remember to explore further and experiment with different image processing techniques or feature extraction algorithms to enhance the performance and accuracy of your fingerprint authentication system.

Remember to save your files and run them based on the provided instructions to have a better understanding of how they work. Feel free to adapt the code and modify it to fit your specific use case.

Published: 20 July 2022