Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques

Biometric systems identify individuals by comparison of the individual biometric traits, such as the fingerprint patterns. In the literature, many relevant methods are based on the localization of a reference \u201cpivot\u201d point of the fingerprint, called principal singular point (PSP). Most of the time, the PSP is selected from the list of the estimated singular points (SPs) that are identified by specific local patterns of the fingerprint ridges, called cores and deltas. The challenge is to provide an automatic method capable to select the same PSP from different images of the same fingertip. In this paper, we propose a technique that estimates the position of all the singular points by processing the global structure of the ridges and extracting a specific set of features. The selection of the reference point from the candidate list is then obtained by processing the extracted features with computational intelligence classification techniques. Experiments show that the method is accurate and it can be applied on contact and contact-less image types

Neural-based quality measurement of fingerprint images in contactless biometric systems

Traditional fingerprint biometric systems capture the user fingerprint images by a contact-based sensor. Differently, contactless systems aim to capture the fingerprint images by an approach based on a vision system without the need of any contact of the user with the sensor. The user finger is placed in front of a special CCD-based system that captures the pattern of ridges and valleys of the fingertips. This approach is less constrained by the point of view of the user, but it requires much more capability of the system to deal with the focus of the moving target, the illumination problems and the complexity of the background in the captured image. During the acquisition procedure, the quality of each frame must be carefully evaluated in order to extract only the correct frames with valuable biometric information from the sequence. In this paper, we present a neural-based approach for the quality estimation of the contactless fingertips images. The application of the neural classification models allowed for a relevant reduction of the computational complexity permitting the application in realtime. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters

An overview of touchless 2D fingerprint recognition

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

Toward unconstrained fingerprint recognition : a fully touchless 3-D system based on two views on the move

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices

Realization of fingerprint scanner

Diplomová práce se zabývá problematikou snímání otisku lidského prstu, která je v současnosti velmi aktuální a představuje nejrozšířenější biometrickou technologii. Teoretická část práce seznamuje čtenáře se základy daktyloskopie a biometrie a pojednává o technologiích využívaných ke snímání otisků, o metodách předzpracování pořízených obrazů a o komerčně dostupných bezkontaktních optických snímačích. Praktickou částí práce je realizace bezkontaktního optického snímače založeného na minipočítači Raspberry Pi, implementace algoritmů pro zpracování snímků v jazyce Python a testování zařízení z hlediska kvality získaných otisků.This master’s thesis deals with the issue of scanning human fingerprints, which is currently very topical and represents the most widespread biometric technology. The theoretical part of the work acquaints the reader with basics of dactyloscopy and biometrics and concerns technologies used for fingerprinting, image preprocessing methods and commercially available contactless optical scanners. The practical part is a realisation of a contactless optical scanner based on a Raspberry Pi minicomputer, implementation of preprocessing algorithms in Python and testing of the device from the perspective of image quality.