Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 6 Issue 6
November-December 2025

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An Analysis of Micro Texture Feature Extraction in Finger Print Image Identification

Author(s) Mrs. S. SUBHASHINI, Dr. P. UMA MAHESWARI
Country India
Abstract Fingerprint recognition remains one of the most reliable and widely adopted biometric authentication techniques due to its stability, uniqueness, and permanence. While traditional fingerprint analysis relies mainly on minutiae features such as ridge endings and bifurcations, micro-texture characteristics provide deeper discriminatory power, especially in low-quality or partial prints. This analysis focuses on the extraction and analysis of micro-texture features—including local ridge orientation, ridge frequency, pore distribution, gray-level statistical measures, and Local Binary Patterns (LBP)—from fingerprint images obtained from publicly available datasets such as FVC2002 and FVC2004. Around 80–100 high-resolution (500 dpi) fingerprint samples were processed. Image enhancement, segmentation, and feature extraction algorithms were implemented in MATLAB/Python to quantify micro-texture attributes. The extracted features were analyzed to evaluate intra-class consistency and inter-class discriminability. Results demonstrate that micro-texture descriptors improve fingerprint classification and matching accuracy, particularly for smudged, dry, or noisy finger prints. The study highlights the significance of integrating micro-texture features with traditional minutiae-based systems for robust biometric authentication.
Keywords Fingerprint recognition, Micro-texture features, Local Binary Pattern (LBP), Ridge orientation, Ridge frequency, Gray-level features.
Field Computer Applications
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-09
DOI https://doi.org/10.63363/aijfr.2025.v06i06.2401
Short DOI https://doi.org/hbf943
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E-ISSN 3048-7641
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