MATLAB Code Implementation for Fingerprint Image Preprocessing

This document discusses the sequential steps involved in fingerprint image preprocessing. Fingerprint image preprocessing serves as a critical procedure for preparing fingerprint images for subsequent feature extraction and matching tasks. The program comprises four primary stages: segmentation, binarization, denoising, and thinning. During the segmentation phase, the fingerprint image is partitioned into distinct regions using techniques like region-based analysis or edge detection algorithms (e.g., Sobel or Canny operators) to facilitate independent processing of each area. This can be implemented in MATLAB using regionprops function or custom thresholding methods to isolate fingerprint areas from the background. The binarization step converts the grayscale image into a binary format using global or adaptive thresholding approaches. MATLAB's imbinarize function with Otsu's method is commonly employed to enhance ridge-valley contrast, where pixel values above a calculated threshold become white (ridges) and below become black (valleys). In the denoising stage, appropriate filters such as median filtering (medfilt2) or Wiener filtering (wiener2) are applied to eliminate noise while preserving ridge structures. Directional filters or Gabor filters may also be implemented to enhance ridge patterns parallel to specific orientations. The final thinning process reduces ridge lines to single-pixel width using morphological operations like bwmorph with 'thin' parameter, implementing Zhang-Suen or Guo-Hall algorithms to skeletonize ridges while maintaining connectivity. This enables precise extraction of minutiae points (ridge endings and bifurcations) for subsequent analysis. Through this integrated pipeline, fingerprint images are effectively preprocessed to enhance feature visibility, remove artifacts, and standardize structures, thereby providing optimized input for fingerprint recognition systems. Code implementation typically involves sequential function calls with parameter tuning based on image quality assessments.