MATLAB Code Implementation for Fingerprint Recognition

This document discusses how to implement a fingerprint recognition program using MATLAB. The program performs fingerprint image binarization, thinning, center calculation, database storage, and fingerprint matching. During binarization, the fingerprint image is converted into black and white pixels using thresholding techniques, typically implemented with MATLAB's imbinarize() function or custom Otsu's method. The thinning process reduces pixel density using skeletonization algorithms like Zhang-Suen or Guo-Hall to enhance feature extraction accuracy. For center calculation, mathematical formulas involving centroid computation or geometric center detection are applied to determine the precise central point of fingerprint patterns. Database storage utilizes MATLAB's database toolbox or file I/O operations to save fingerprint templates efficiently. The matching phase employs correlation-based comparison or minutiae-based algorithms to verify fingerprint identities. This program serves as a robust tool for fingerprint recognition with applications in security authentication, criminal investigation, and biometric systems. Implementation details include optimized image processing workflows and efficient data management techniques for real-world deployment.