An application of fuzzy logic and neural network to fingerprint recognition

[[abstract]]The correct minutiae extraction is very important in an automatic fingerprint identification system. However, the presence of noise in poor-quality images can cause many extraction faults, such as the dropping of true minutiae and inclusion of false minutiae. Most fingerprint identification systems are based on precise mathematical models, but they cannot handle such faults properly. As human beings are good at recognizing fingerprint patterns, a human-like method is applied. The paper presents an adaptive fuzzy logic and neural network method which has variable fault tolerance. Our experimental results show that this fingerprint identification method is robust, reliable and rapid.[[conferencetype]]國際[[conferencedate]]20050518~20050520[[conferencelocation]]Sapporo, Japa

Authentication Using Sparse Modeling of Fingerprint Images

Fingerprint sensors often have difficulty authenticating users if the user’s fingers are wet or moist, e.g., due to sweat. This disclosure presents robust techniques for fingerprint identification based on the K-SVD algorithm, which is a technique to represent images in a sparse manner. A K-SVD dictionary is created out of enrolled fingerprint images. A fingerprint that is to be authenticated is segmented into blocks, and each block is projected against the dictionary. A heat map of highest projection coefficients is formed, and overall match-score is calculated. The overall match-score is used to authenticate the fingerprint. The dictionary stores the essential features of the enrolled fingerprints, and the enrolled fingerprint images are deleted. Fingerprint authentication is made possible without actual storage of the enrolled fingerprints, which serves to improve security

Development of an Improved Fingerprint Feature Extraction Algorithm for Personal Verification

New and sophisticated technologies are regularly developed to counter every new wave of breaches in data security. At the heart of some of these technologies is the personal verification system that rests on the oars of biometrics. Biometric systems use unique physical and behavioral traits for identification or verification. In this paper, an improved fingerprint feature extraction algorithm for personal verification is proposed. The improved fingerprint feature extraction algorithm is capable of recognizing authorized individuals and differentiating them from fraudulent imposters. The input images were preprocessed before extracting robust features for matching. Euclidean distance was used for classification. The proposed system was tested using the fingerprint images of fifty registered individuals and thirty imposters. The results obtained were a False Acceptance Rate and False Rejection Rate of 16% and 24% respectively. It is also faster than other feature extraction algorithms by forty (40) seconds Keywords: Fingerprint, biometrics, robust features, division into blocks, ridge pattern, euclidean distance, personal verification, feature extraction, classification

Study of Fingerprint Enhancement and Matching

Fingerprint is the oldest and popular form of bio-metric identification. Extract Minutiae is most used method for automatic fingerprint matching, every person fingerprint has some unique characteristics called minutiae. But studying the extract minutiae from the fingerprint images and matching it with database is depend on the image quality of finger impression. To make sure the performance of finger impression identification we have to robust the quality of fingerprint image by a suitable fingerprint enhancement algorithm. Here we work with a quick finger impression enhancement algorithm that improve the lucidity of valley and ridge structure based on estimated local orientation and frequency. After enhancement of sample fingerprint, sample fingerprint is matched with the database fingerprints, for that we had done feature extraction, minutiae representation and registration. But due to Spurious and missing minutiae the accuracy of fingerprint matching affected. We had done a detail relevant finger impression matching method build on the Shape Context descriptor, where the hybrid shape and orientation descriptor solve the problem. Hybrid shape descriptor filter out the unnatural minutia paring and ridge orientation descriptor improve the matching score. Matching score is generated and utilized for measuring the accuracy of execution of the proposed algorithm. Results demonstrated that the algorithm is exceptionally satisfactory for recognizing fingerprints acquired from diverse sources. Experimental results demonstrate enhancement algorithm also improves the matching accuracy

A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design

Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio signature, the second step usually requires knowledge about database and quick-search algorithms. Though this technique offers a wide range of real-world applications, to the best of the authors' knowledge, a comprehensive survey of existing algorithms appeared more than eight years ago. Thus, in this paper, we present a more up-to-date review and, for emphasizing on the audio signal processing aspect, we focus our state-of-the-art survey on the fingerprint design step for which various audio features and their tractable statistical models are discussed.Comment: http://www.iaria.org/conferences2015/PATTERNS15.html ; Seventh International Conferences on Pervasive Patterns and Applications (PATTERNS 2015), Mar 2015, Nice, Franc

Fingerprint Verification System Using Support Vector Machine

Efficient fingerprint verification system is needed in many places for personal identification to access physical facilities, information etc. This paper proposes robust verification system based on features extracted from human fingerprints and a pattern classifier called Support Vector Machine (SVM). Three set of features are fused together and passed to the classifier. The fused feature is used to train the system for effective verification of users fingerprint images. The result obtained after testing 100 fingerprints is very encouraging

A Robust Document Identification Framework through f-BP Fingerprint

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)The identification of printed materials is a critical and challenging issue for security purposes, especially when it comes to documents such as banknotes, tickets, or rare collectable cards: eligible targets for ad hoc forgery. State-of-the-art methods require expensive and specific industrial equipment, while a low-cost, fast, and reliable solution for document identification is increasingly needed in many contexts. This paper presents a method to generate a robust fingerprint, by the extraction of translucent patterns from paper sheets, and exploiting the peculiarities of binary pattern descriptors. A final descriptor is generated by employing a block-based solution followed by principal component analysis (PCA), to reduce the overall data to be processed. To validate the robustness of the proposed method, a novel dataset was created and recognition tests were performed under both ideal and noisy conditions.Peer reviewedFinal Published versio