Investigating the impact of thresholding and thinning methods on the performance of partial fingerprint identification systems: a review

Abstract

A fingerprint identification system is an application of pattern recognition and image processing. The performance of a fingerprint-based biometric system relies on pre-processing techniques employed on fingerprint images. Especially, thresholding and thinning methods are used to detect minutiae points representing local features and are often utilized to identify a person uniquely. However, studies on partial fingerprints exposed the MasterPrint vulnerability for partial fingerprint identification systems wherein the system performs non-unique user identification. The thresholding and thinning techniques may lead to spurious minutiae generation and stimulate huge MasterPrints. Here, we investigate the impact of thresholding and thinning methods on identification accuracy and the percentage of MasterPrint generated using a partial fingerprint identification method. The experiments comprise four thresholding methods, namely, iterative optimal thresholding, Otsu’s global image thresholding, Niblack local thresholding, and Bernsen’s local image thresholding. Furthermore, it employs four thinning methods, namely, Khalid, Mariusz, Marek thinning algorithm, Khalid, Marek, Mariusz, Marcin thinning algorithm, Hilditch thinning algorithm, and Stentiford thinning algorithm. The results demonstrate that the identification accuracy and percentage of MasterPrint generated varies significantly by replacing the underlying pre-processing methods. Consequently, each combination of thresholding and thinning methods might not be suitable for user identification in high-security applications using a partial fingerprint identification method. The investigation outcomes provide the guidelines to demonstrate the robustness of a partial fingerprint identification method.