Fingerprint Recognition Algorithm Implementation Using MATLAB

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Resource Overview

With the continuous advancement of biometric technology, it has been discovered that each individual's fingerprint possesses uniqueness and permanence. Consequently, fingerprint recognition technology has evolved into a novel identity authentication method, demonstrating strong potential to replace traditional identification approaches due to its excellent security and reliability. This article systematically outlines the fundamental steps of fingerprint recognition: fingerprint image preprocessing, feature extraction, and fingerprint matching. The preprocessing phase covers normalization, image enhancement, binarization, and thinning techniques, ultimately producing a refined binary image with single-pixel width. Fingerprint matching is then performed by analyzing distinctive endpoint and crossover point features. The complete algorithmic pipeline is implemented through MATLAB programming, providing practical insights into image processing operations and pattern recognition methodologies.

Detailed Documentation

As biometric technology continues to develop, researchers have confirmed that each person's fingerprint exhibits unique and immutable characteristics. This has positioned fingerprint recognition technology as an emerging identification method that shows significant potential to supersede traditional authentication systems due to its superior security and reliability. This paper provides a comprehensive breakdown of fingerprint recognition's core procedures to ensure readers gain thorough technical understanding. The fingerprint image preprocessing section details sequential operations including normalization (adjusting image contrast and intensity), image enhancement techniques (using Gabor filters or Fourier transforms to improve ridge clarity), binarization (converting grayscale images to binary using thresholding algorithms), and thinning processing (applying morphological operations to achieve single-pixel-width skeletons). These preprocessing steps generate optimized binary images for subsequent feature extraction. The feature extraction phase explores specialized algorithms for identifying minutiae points, particularly endpoints and bifurcations, through ridge tracking and crossing number calculations. The final matching stage implements similarity assessment strategies by comparing extracted features against template databases using distance metrics or neural network classifiers. To facilitate practical implementation, MATLAB code examples demonstrate complete algorithm workflow including key functions like imbinarize for thresholding, bwmorph for thinning, and regionprops for feature measurement. This work aims to deliver an in-depth technical exposition of fingerprint recognition technology to support further research and real-world applications in biometric security systems.

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