555 research outputs found
An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics
Biometric systems have to address many requirements, such as large population
coverage, demographic diversity, varied deployment environment, as well as
practical aspects like performance and spoofing attacks. Traditional unimodal
biometric systems do not fully meet the aforementioned requirements making them
vulnerable and susceptible to different types of attacks. In response to that,
modern biometric systems combine multiple biometric modalities at different
fusion levels. The fused score is decisive to classify an unknown user as a
genuine or impostor. In this paper, we evaluate combinations of score
normalization and fusion techniques using two modalities (fingerprint and
finger-vein) with the goal of identifying which one achieves better improvement
rate over traditional unimodal biometric systems. The individual scores
obtained from finger-veins and fingerprints are combined at score level using
three score normalization techniques (min-max, z-score, hyperbolic tangent) and
four score fusion approaches (minimum score, maximum score, simple sum, user
weighting). The experimental results proved that the combination of hyperbolic
tangent score normalization technique with the simple sum fusion approach
achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
Signal-quality awareness has been found to increase recognition rates and to
support decisions in multisensor environments significantly. Nevertheless,
automatic quality assessment is still an open issue. Here, we study the
orientation tensor of fingerprint images to quantify signal impairments, such
as noise, lack of structure, blur, with the help of symmetry descriptors. A
strongly reduced reference is especially favorable in biometrics, but less
information is not sufficient for the approach. This is also supported by
numerous experiments involving a simpler quality estimator, a trained method
(NFIQ), as well as the human perception of fingerprint quality on several
public databases. Furthermore, quality measurements are extensively reused to
adapt fusion parameters in a monomodal multialgorithm fingerprint recognition
environment. In this study, several trained and nontrained score-level fusion
schemes are investigated. A Bayes-based strategy for incorporating experts past
performances and current quality conditions, a novel cascaded scheme for
computational efficiency, besides simple fusion rules, is presented. The
quantitative results favor quality awareness under all aspects, boosting
recognition rates and fusing differently skilled experts efficiently as well as
effectively (by training).Comment: Published at IEEE Transactions on Information Forensics and Securit
Review of Multimodal Biometric Identification Using Hand Feature and Face
In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits
Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation
Im ersten Teil dieser Arbeit wird Fingerwachstum
untersucht und eine Methode zur Vorhersage von Wachstum
wird vorgestellt. Die EffektivitÀt dieser Methode wird
mittels mehrerer Tests validiert. Vorverarbeitung von
Fingerabdrucksbildern wird im zweiten Teil behandelt
und neue Methoden zur SchÀtzung des Orientierungsfelds
und der Ridge-Frequenz sowie zur Bildverbesserung
werden vorgestellt: Die Line Sensor Methode zur
OrientierungsfeldschÀtzung, gebogene Regionen zur
Ridge-Frequenz-SchÀtzung und gebogene Gabor Filter zur
Bildverbesserung. Multi-level Jugdment Aggregation wird
eingefĂŒhrt als Design Prinzip zur Kombination mehrerer
Methoden auf mehreren Verarbeitungsstufen. SchlieĂlich
wird Score Neubewertung vorgestellt, um Informationen
aus der Vorverarbeitung mit in die Score Bildung
einzubeziehen. Anhand eines Anwendungsbeispiels wird
die Wirksamkeit dieses Ansatzes auf den verfĂŒgbaren
FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the
thesis and a method for growth prediction is presented.
The effectiveness of the method is validated in several
tests. Fingerprint image preprocessing is discussed in
the second part and novel methods for orientation field
estimation, ridge frequency estimation and image
enhancement are proposed: the line sensor method for
orientation estimation provides more robustness to
noise than state of the art methods. Curved regions are
proposed for improving the ridge frequency estimation
and curved Gabor filters for image enhancement. The
notion of multi-level judgment aggregation is
introduced as a design principle for combining
different methods at all levels of fingerprint image
processing. Lastly, score revaluation is proposed for
incorporating information obtained during preprocessing
into the score, and thus amending the quality of the
similarity measure at the final stage. A sample
application combines all proposed methods of the second
part and demonstrates the validity of the approach by
achieving massive verification performance improvements
in comparison to state of the art software on all
available databases of the fingerprint verification
competitions (FVC)
Mixing Biometric Data For Generating Joint Identities and Preserving Privacy
Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation
Latent fingerprint identification is attracting increasing interest because of its important role
in law enforcement. Although the use of various fingerprint features might be required for successful latent
fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform
other methods. However, as many fingerprint feature representations exist, we sought to determine if the
selection of feature representation has an impact on the performance of automated fingerprint identification
systems. In this paper, we review the most prominent fingerprint feature representations reported in the
literature, identify trends in fingerprint feature representation, and observe that representations designed for
verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of
the most popular fingerprint feature representations over a common latent fingerprint database. Therefore,
we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification
in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our
experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than
10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating
the need of new minutia descriptors for latent fingerprint identification.This work was supported in part by the National Council of Science and Technology of Mexico (CONACYT) under Grant PN-720 and
Grant 63894
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