Fingerprint Image Preprocessing

This content addresses fingerprint image preprocessing for fingerprint recognition systems, encompassing segmentation, binarization, and thinning operations. In fingerprint recognition technology, preprocessing serves as a critical step that optimizes and enhances raw fingerprint images through these operations to improve subsequent feature extraction and matching accuracy. Segmentation involves isolating individual fingerprint ridges from the background image, typically implemented using region-growing algorithms or edge detection methods like Sobel or Canny operators. Binarization converts grayscale images into binary black-and-white images using thresholding techniques (e.g., Otsu's method or adaptive thresholding) to emphasize ridge patterns. Thinning employs morphological operations (such as Zhang-Suen or Guo-Hall algorithms) to reduce ridge widths to single-pixel lines while preserving topology. These preprocessing steps create optimized images for subsequent feature extraction and matching, thereby enhancing the accuracy and reliability of fingerprint recognition systems. Common implementations involve OpenCV functions like cv2.threshold() for binarization and cv2.ximgproc.thinning() for skeletonization.