Enhanced Fingerprint Recognition using a Hybrid Technique of Morphological Minutiae Extraction and GLCM Features from Canny Filtering

Abstract

This review paper presents a comprehensive analysis of an enhanced fingerprint recognition system that integrates Morphological Minutiae Extraction with Gray-Level Co-occurrence Matrix (GLCM) features derived from Canny-filtered images. Biometric recognition, particularly fingerprint-based systems, offers a robust and secure alternative to traditional identification methods due to its reliance on unique and stable biological traits. Despite its widespread use, traditional fingerprint recognition faces challenges such as low-quality images, false acceptance/rejection rates, and spoofing. The aim of this study is to propose a hybrid methodology that addresses these limitations by leveraging Canny filtering for synergistic image enhancement, enabling precise morphological extraction of minutiae (ridge endings and bifurcations) and robust computation of GLCM texture features (contrast, correlation, energy, homogeneity). Future research directions include optimizing hybrid approaches, adaptive parameter tuning, and exploring advanced machine learning techniques to further improve performance and efficiency..