Fingerprint Recognition MATLAB Implementation with Code Description

This article presents a fingerprint recognition program implemented in MATLAB. The complete workflow consists of the following processing stages:

1. Fingerprint Enhancement: This stage employs image processing techniques to improve fingerprint quality, typically using Gabor filters or Fourier transform-based methods to enhance ridge clarity and reduce noise for subsequent processing.

2. Orientation and Ridge Estimation: Through gradient-based calculations or filter bank approaches, this module determines ridge directions and locations, providing fundamental information for binary masking and feature extraction algorithms.

3. Binary Masking: The grayscale fingerprint image is converted to a binary representation using adaptive thresholding techniques, creating a clear distinction between ridges and valleys to facilitate feature analysis.

4. Feature Extraction: From the binarized image, key fingerprint characteristics such as minutiae points (ridge endings and bifurcations) are detected and encoded into feature vectors using specialized algorithms for pattern matching.

5. Classification: The final stage compares extracted features from input images using matching algorithms (e.g., Euclidean distance or neural networks) to determine whether fingerprints belong to the same individual or different persons.

This implementation provides an efficient framework for fingerprint image recognition with applications in security systems, access control, and attendance monitoring. The modular code structure allows for customization and integration with various biometric applications.