Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

TL;DR: A review and categorize the vast number of fingerprint matching methods proposed in the specialized literature, focusing on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency.

TL;DR: The aim of this paper is to review various recently work on fingerprint recognition system and explain fingerprint recognition stages step by step and give summaries of fingerprint databases with characteristics.

TL;DR: This paper presents a review of a large number of techniques present in the literature for extracting fingerprint minutiae, broadly classified as those working on binarized images and those that work on gray scale images directly.

TL;DR: Experimental results show that the proposed high-resolution palmprint recognition system compares very favorably with the state of the art, being based on a local matching strategy and an efficient and compact representation of the minutiae.

TL;DR: A comprehensive survey of thinning methodologies, including iterative deletion of pixels and nonpixel-based methods, is presented and the relationships among them are explored.

TL;DR: An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner and an alignment-based elastic matching algorithm has been developed.

TL;DR: A reliable method for extracting structural features from fingerprint images is presented and a “goodness index” (GI) which compares the results of automatic extraction with manually extracted ground truth is evaluated.

TL;DR: Introduction to Fingerprint Recognition, U.J. Erol Fingerprint Feature Processing Techniques and Poroscopy, A.R. Howell Neural Networks for Face recognition, and Ongun Introduction to Face Recognition.