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

Método para compatibilidade entre impressões digitais com e sem contato

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia e Instituto de Ciências Exatas, 2014.Soluções em biometria por impressões sem contato têm sido propostas com o objetivo de se superar os problemas intrínsecos relacionados às tecnologias que exigem o contato. Para desfrutarmos das vantagens operacionais dos digitalizadores sem toque, as imagens resultantes deste tipo de captura devem ser processadas a fim de se tornarem similares àquelas obtidas por meio dos digitalizadores tradicionais baseados em contato, visto que os sistemas legados possuem bancos de mais de 200 milhões de imagens adquiridas com contato, as quais são diariamente utilizadas para efetuar identificações. O presente trabalho apresenta uma solução em duas etapas visando a compatibilidade entre essas duas tecnologias, onde: (I) Procura reproduzir a textura de impressões digitais legadas; e (II) simular o processo de rolagem unha-a-unha. A partir de testes, comprovamos que 90% das impressões adquiridas sem toque e então processadas são consideradas boas, muito boas ou excelentes e que sistemas já existentes para impressões digitais com toque retornaram um EER (Equal Error Rate) em torno de 0.34% para casamentos entre impressões digitais sem contato multivista e de 13% entre impressões digitais com e sem contato. Desta forma, demonstrando a viabilidade do método aqui proposto.Touchless multiview fingerprinting technology has recently been proposed as an alternative to overcome the intrinsic problems of traditional touch-based systems. However, since touch-based fingerprinting technology is undoubtedly the most used nowadays, images captured by touchless devices must be further processed in order to become similar (and therefore compatible) to those obtained by conventional scanners. This work presents a two-step solution in order to make touch and touchless technologies work together. Test results have shown that 90% of fingerprints acquired without touch and then processed were considered good, very good or excellent and existing touch based systems returned an EER around 0.34% for matchings between touchless multi view fingerprints and 13% between contact and contactless. Therefore, demonstrating the feasibility of the proposed method

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

CONTACTLESS FINGERPRINT BIOMETRICS: ACQUISITION, PROCESSING, AND PRIVACY PROTECTION

Biometrics is defined by the International Organization for Standardization (ISO) as \u201cthe automated recognition of individuals based on their behavioral and biological characteristics\u201d Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA. The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals. Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services. Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions. Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used. In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples. Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists. In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data. The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications. Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability. The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography. The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage

BIOMETRIC TECHNOLOGIES FOR AMBIENT INTELLIGENCE

Il termine Ambient Intelligence (AmI) si riferisce a un ambiente in grado di riconoscere e rispondere alla presenza di diversi individui in modo trasparente, non intrusivo e spesso invisibile. In questo tipo di ambiente, le persone sono circondate da interfacce uomo macchina intuitive e integrate in oggetti di ogni tipo. Gli scopi dell\u2019AmI sono quelli di fornire un supporto ai servizi efficiente e di facile utilizzo per accrescere le potenzialit\ue0 degli individui e migliorare l\u2019interazioni uomo-macchina. Le tecnologie di AmI possono essere impiegate in contesti come uffici (smart offices), case (smart homes), ospedali (smart hospitals) e citt\ue0 (smart cities). Negli scenari di AmI, i sistemi biometrici rappresentano tecnologie abilitanti al fine di progettare servizi personalizzati per individui e gruppi di persone. La biometria \ue8 la scienza che si occupa di stabilire l\u2019identit\ue0 di una persona o di una classe di persone in base agli attributi fisici o comportamentali dell\u2019individuo. Le applicazioni tipiche dei sistemi biometrici includono: controlli di sicurezza, controllo delle frontiere, controllo fisico dell\u2019accesso e autenticazione per dispositivi elettronici. Negli scenari basati su AmI, le tecnologie biometriche devono funzionare in condizioni non controllate e meno vincolate rispetto ai sistemi biometrici comunemente impiegati. Inoltre, in numerosi scenari applicativi, potrebbe essere necessario utilizzare tecniche in grado di funzionare in modo nascosto e non cooperativo. In questo tipo di applicazioni, i campioni biometrici spesso presentano una bassa qualit\ue0 e i metodi di riconoscimento biometrici allo stato dell\u2019arte potrebbero ottenere prestazioni non soddisfacenti. \uc8 possibile distinguere due modi per migliorare l\u2019applicabilit\ue0 e la diffusione delle tecnologie biometriche negli scenari basati su AmI. Il primo modo consiste nel progettare tecnologie biometriche innovative che siano in grado di funzionare in modo robusto con campioni acquisiti in condizioni non ideali e in presenza di rumore. Il secondo modo consiste nel progettare approcci biometrici multimodali innovativi, in grado di sfruttare a proprio vantaggi tutti i sensori posizionati in un ambiente generico, al fine di ottenere un\u2019elevata accuratezza del riconoscimento ed effettuare autenticazioni continue o periodiche in modo non intrusivo. Il primo obiettivo di questa tesi \ue8 la progettazione di sistemi biometrici innovativi e scarsamente vincolati in grado di migliorare, rispetto allo stato dell\u2019arte attuale, la qualit\ue0 delle tecniche di interazione uomo-macchine in diversi scenari applicativi basati su AmI. Il secondo obiettivo riguarda la progettazione di approcci innovativi per migliorare l\u2019applicabilit\ue0 e l\u2019integrazione di tecnologie biometriche eterogenee negli scenari che utilizzano AmI. In particolare, questa tesi considera le tecnologie biometriche basate su impronte digitali, volto, voce e sistemi multimodali. Questa tesi presenta le seguenti ricerche innovative: \u2022 un metodo per il riconoscimento del parlatore tramite la voce in applicazioni che usano AmI; \u2022 un metodo per la stima dell\u2019et\ue0 dell\u2019individuo da campioni acquisiti in condizioni non-ideali nell\u2019ambito di scenari basati su AmI; \u2022 un metodo per accrescere l\u2019accuratezza del riconoscimento biometrico in modo protettivo della privacy e basato sulla normalizzazione degli score biometrici tramite l\u2019analisi di gruppi di campioni simili tra loro; \u2022 un approccio per la fusione biometrica multimodale indipendente dalla tecnologia utilizzata, in grado di combinare tratti biometrici eterogenei in scenari basati su AmI; \u2022 un approccio per l\u2019autenticazione continua multimodale in applicazioni che usano AmI. Le tecnologie biometriche innovative progettate e descritte in questa tesi sono state validate utilizzando diversi dataset biometrici (sia pubblici che acquisiti in laboratorio), i quali simulano le condizioni che si possono verificare in applicazioni di AmI. I risultati ottenuti hanno dimostrato la realizzabilit\ue0 degli approcci studiati e hanno mostrato che i metodi progettati aumentano l\u2019accuratezza, l\u2019applicabilit\ue0 e l\u2019usabilit\ue0 delle tecnologie biometriche rispetto allo stato dell\u2019arte negli scenari basati su AmI.Ambient Intelligence (AmI) refers to an environment capable of recognizing and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. In this environment, people are surrounded by intelligent intuitive interfaces that are embedded in all kinds of objects. The goals of AmI are to provide greater user-friendliness, more efficient services support, user-empowerment, and support for human interactions. Examples of AmI scenarios are smart cities, smart homes, smart offices, and smart hospitals. In AmI, biometric technologies represent enabling technologies to design personalized services for individuals or groups of people. Biometrics is the science of establishing the identity of an individual or a class of people based on the physical, or behavioral attributes of the person. Common applications include: security checks, border controls, access control to physical places, and authentication to electronic devices. In AmI, biometric technologies should work in uncontrolled and less-constrained conditions with respect to traditional biometric technologies. Furthermore, in many application scenarios, it could be required to adopt covert and non-cooperative technologies. In these non-ideal conditions, the biometric samples frequently present poor quality, and state-of-the-art biometric technologies can obtain unsatisfactory performance. There are two possible ways to improve the applicability and diffusion of biometric technologies in AmI. The first one consists in designing novel biometric technologies robust to samples acquire in noisy and non-ideal conditions. The second one consists in designing novel multimodal biometric approaches that are able to take advantage from all the sensors placed in a generic environment in order to achieve high recognition accuracy and to permit to perform continuous or periodic authentications in an unobtrusive manner. The first goal of this thesis is to design innovative less-constrained biometric systems, which are able to improve the quality of the human-machine interaction in different AmI environments with respect to the state-of-the-art technologies. The second goal is to design novel approaches to improve the applicability and integration of heterogeneous biometric systems in AmI. In particular, the thesis considers technologies based on fingerprint, face, voice, and multimodal biometrics. This thesis presents the following innovative research studies: \u2022 a method for text-independent speaker identification in AmI applications; \u2022 a method for age estimation from non-ideal samples acquired in AmI scenarios; \u2022 a privacy-compliant cohort normalization technique to increase the accuracy of already deployed biometric systems; \u2022 a technology-independent multimodal fusion approach to combine heterogeneous traits in AmI scenarios; \u2022 a multimodal continuous authentication approach for AmI applications. The designed novel biometric technologies have been tested on different biometric datasets (both public and collected in our laboratory) simulating the acquisitions performed in AmI applications. Results proved the feasibility of the studied approaches and shown that the studied methods effectively increased the accuracy, applicability, and usability of biometric technologies in AmI with respect to the state-of-the-art

Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks