"Matlab实现指纹识别算法及其可行性分析"

可行性和实用性， it has become one of the most popular and reliable personal identity authentication technologies. This article mainly processes fingerprint images in three aspects: image preprocessing, feature extraction, and feature matching. Image preprocessing includes four steps: image segmentation, filtering enhancement, binarization, and thinning. After preprocessing the fingerprint image, the redundant parts of the original image are removed, facilitating subsequent recognition processing. Feature extraction mainly extracts the endpoints and bifurcation points of the thinned fingerprint image. Feature matching uses the images of two fingerprints to compare feature points to determine if the two images come from the same finger. This article provides Matlab programs and processing results for fingerprint image preprocessing, feature extraction, and feature matching. The results prove that the processing results of these algorithms implemented in Matlab are ideal, meeting the feasibility and applicability of recognition. Keywords: segmentation, binarization, thinning, feature point extraction, matching, Matlab. In summary, the implementation of fingerprint recognition algorithm in Matlab is highly effective and reliable. The preprocessing, feature extraction, and feature matching processes all contribute to accurately identifying fingerprints and determining if two images belong to the same finger. The use of Matlab in implementing these algorithms has proven to be successful in achieving the desired results, highlighting the feasibility and applicability of fingerprint recognition technology. By following the steps outlined in this study, users can successfully implement fingerprint recognition algorithms using Matlab for various applications, ensuring secure and reliable personal identity authentication.