1,721,046 research outputs found
Exploring the Power of Simplicity: A New State-of-the-Art in Fingerprint Orientation Field Estimation
Full text linkFingerprint recognition is an important tool for personal identification due to its versatility, user-friendliness, and accuracy. Fingerprint orientation field estimation, a crucial step in fingerprint feature extraction, significantly impacts recognition performance. While numerous methods have been proposed, achieving state-of-the-art accuracy often comes at the cost of increased complexity, hindering practical implementation. This work addresses this challenge by introducing two novel, simple, and efficient fingerprint orientation field estimation methods: GBFOE and SNFOE. Both methods adhere to the KISS (Keep It Simple and Straightforward) principle, achieving remarkable performance on publicly available benchmarks. GBFOE outperforms all local methods and rivals more complex approaches, while SNFOE establishes itself as a new state-of-the-art, achieving the highest accuracy on all datasets in both evaluated benchmarks. Surpassing methods designed specifically for latent fingerprints, SNFOE demonstrates exceptional performance on this challenging task within the evaluated benchmarks, highlighting its generalizability despite not being trained on such data. These results underline the potential of simple and efficient methods in fingerprint orientation field estimation, paving the way for practical and resource-efficient fingerprint recognition systems. An open-source Python implementation of both methods is available, fostering further research and development in this field
No Feature Left Behind: Filling the Gap in Fingerprint Frequency Estimation
Full text linkFingerprint recognition remains a cornerstone of biometric identification systems. However, the critical task of ridge-line frequency estimation has been surprisingly underexplored. To address this research gap, I introduce the first publicly available benchmark for fingerprint frequency estimation, a pioneering resource that includes both high- and low-quality fingerprints with meticulously labeled ground truth features: segmentation masks, orientation fields, and frequency maps. Furthermore, two novel frequency estimation methods are proposed: one that significantly enhances a well-known frequency estimation method based on traditional image processing techniques and another that, for the first time, applies deep learning to this context. Experimental results on the new benchmark demonstrate that both methods surpass existing state-of-the-art techniques, particularly in challenging low-quality fingerprint scenarios. By providing an open-source implementation and a comprehensive benchmark, this work sets a new standard for the evaluation of frequency estimation methods, fostering further research and development in this crucial area of fingerprint recognition
Unveiling the Power of Simplicity: Two Remarkably Effective Methods for Fingerprint Segmentation
Full text linkAccurate fingerprint segmentation is crucial for reliable fingerprint recognition systems. This paper presents two novel segmentation methods, GMFS and SUFS, inspired by the KISS (Keep It Simple and Straightforward) principle. Both methods, evaluated on a public benchmark and compared to eighteen state-of-the-art approaches, excel in terms of accuracy, while maintaining simplicity and computational efficiency. GMFS utilizes a single handcrafted feature for straightforward yet effective fingerprint segmentation, achieving superior performance compared to previously reported traditional methods. SUFS employs a simplified U-net architecture for end-to-end segmentation, demonstrating remarkable performance: it achieves an average classification error rate of 1.51% across the entire benchmark, with an improvement of over 40% compared to the previously best-performing method. Furthermore, despite being trained on a relatively small dataset, it exhibits significant generalization capabilities, effectively segmenting fingerprints from very different acquisition technologies without requiring fine-tuning. An open-source Python implementation of both methods is available, fostering further research and development in the field of fingerprint recognition
SFinGe
No full textSFinGe (Synthetic Fingerprint Generator) is a fingerprint sample synthesis approach developed by the Biometric System Laboratory of the University of Bologna (Italy). It is available as a software program able to generate large databases of images very similar to human’s fingerprints, together with ground-truth data about their characteristics and features. These databases are particularly useful for developing, optimizing and testing fingerprint recognition systems and are being extensively used by industrial, academic and government organizations
Fingerprint Sample Synthesis
No full textFingerprint sample synthesis is the generation of images similar to human’s fingerprints, through parametric models that simulate the main characteristics of such biometric data and their modes of variation. The image synthesis is typically performed by a computer program that, starting from some input parameters, executes a sequence of algorithmic steps that finally produce a synthetic fingerprint image
On the Feasibility of Creating Double-Identity Fingerprints
Full text linkA double-identity fingerprint is a fake fingerprint created by combining features from two different fingers, so that it has a high chance to be falsely matched with fingerprints from both fingers. This paper studies the feasibility of creating double-identity fingerprints by proposing two possible techniques and evaluating to what extent they may be used to fool the state-of-the-art fingerprint recognition systems. The results of systematic experiments suggest that existing algorithms are highly vulnerable to this specific attack (about 90% chance of success at FAR = 0.1%) and that the fingerprint patterns generated might be realistic enough to fool human examiners
Combining biometric matchers by means of machine learning and statistical approaches
No full textIn this paper we study both machine learning and statistical approaches for combining fingerprint matchers of the FVC2006 competition. We investigate not only which is the best fusion approach, but also the correlation among the state-of-the-art matchers for fingerprint verification and scanner interoperability of the fusion techniques. Several tests are performed on all the four FVC2006 datasets, using a leave-one-out dataset testing protocol, i.e., the training phase is conducted on the datasets not used in the testing phase, so it is possible to study the pros and cons of machine learning and statistical approaches when different scanners are used in the training and testing phase.
This work confirms that the fusion of different state-of-the-art fingerprint matchers can lead to a significant performance gain with respect to a single matcher
Introduction to Presentation Attack Detection in Fingerprint Biometrics
Full text linkThis chapter provides an introduction to Presentation Attack Detection (PAD) in fingerprint biometrics, also coined as anti-spoofing, describes early developments in this field, and briefly summarizes recent trends and open issues
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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