Chapter 3: MATLAB-Based Fingerprint Recognition System - Digital Image Recognition Technology Case Study

Fingerprint recognition, as a vital branch of biometric technology, holds significant application value in security authentication and identity verification domains. This chapter demonstrates typical application scenarios of digital image processing technology in biometric identification through a MATLAB-implemented fingerprint recognition system. The core technical pathway of the system comprises four critical stages: Image Preprocessing Phase This stage involves converting images to grayscale and applying histogram equalization to enhance contrast, while using adaptive threshold filtering to eliminate sensor noise. For fingerprint ridge enhancement, orientation field estimation combined with Gabor filter banks effectively highlights periodic characteristics of fingerprint patterns. In MATLAB implementation, functions like rgb2gray() and histeq() handle initial processing, while custom orientation field calculations and Gabor filtering functions enhance ridge structures. Feature Point Extraction Phase An improved Poincare index algorithm locates fingerprint ridge bifurcations and endpoints, constructing feature vectors based on minutiae parameters including orientation angles and coordinate positions. This phase specifically addresses false feature points caused by image quality issues. MATLAB implementation typically involves creating functions for Poincare index calculation and implementing morphological operations to validate genuine minutiae. Feature Matching Phase The matching algorithm based on similarity measurement achieves fingerprint alignment through rotation and translation parameter space search. A two-level matching strategy is employed: initial rapid screening using global features followed by precise matching through local minutiae topological relationships. This can be implemented using MATLAB's image registration tools combined with custom functions for minutiae distance calculations. Performance Optimization Directions The chapter discusses GPU acceleration solutions for processing large-scale fingerprint databases and analyzes potential integration of deep learning methods with traditional image processing approaches, providing technical references for system expansion. MATLAB's Parallel Computing Toolbox and Deep Learning Toolbox offer frameworks for implementing these optimizations. This case study demonstrates how fundamental digital image processing theories translate into practical engineering solutions. Core algorithms such as orientation field calculation and ridge tracking possess transferable value for other biometric recognition systems. The system achieves 92.3% recognition accuracy on the FVC2004 standard test database, validating the solution's feasibility.