6,698 research outputs found
Fingerprint Verification Using Spectral Minutiae Representations
Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points
Curved Gabor Filters for Fingerprint Image Enhancement
Gabor filters play an important role in many application areas for the
enhancement of various types of images and the extraction of Gabor features.
For the purpose of enhancing curved structures in noisy images, we introduce
curved Gabor filters which locally adapt their shape to the direction of flow.
These curved Gabor filters enable the choice of filter parameters which
increase the smoothing power without creating artifacts in the enhanced image.
In this paper, curved Gabor filters are applied to the curved ridge and valley
structure of low-quality fingerprint images. First, we combine two orientation
field estimation methods in order to obtain a more robust estimation for very
noisy images. Next, curved regions are constructed by following the respective
local orientation and they are used for estimating the local ridge frequency.
Lastly, curved Gabor filters are defined based on curved regions and they are
applied for the enhancement of low-quality fingerprint images. Experimental
results on the FVC2004 databases show improvements of this approach in
comparison to state-of-the-art enhancement methods
Neural Networks for Fingerprint Recognition
After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to 20 individuals. The error rate currently achieved is less than 0.5%. Additional results, extensions, and possible applications are also briefly discussed
A Survey of the methods on fingerprint orientation field estimation
Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods
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 Effektivität dieser Methode wird
mittels mehrerer Tests validiert. Vorverarbeitung von
Fingerabdrucksbildern wird im zweiten Teil behandelt
und neue Methoden zur Schätzung des Orientierungsfelds
und der Ridge-Frequenz sowie zur Bildverbesserung
werden vorgestellt: Die Line Sensor Methode zur
Orientierungsfeldschätzung, gebogene Regionen zur
Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur
Bildverbesserung. Multi-level Jugdment Aggregation wird
eingeführt als Design Prinzip zur Kombination mehrerer
Methoden auf mehreren Verarbeitungsstufen. Schließlich
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 verfügbaren
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)
Enhanced Fuzzy Feature Match Algorithm for Mehndi Fingerprints
The performance of biometric system is degraded by the distortions occurred in finger print image acquisition. This paper focuses on nonlinear distortions occurred due to �Mehndi / Heena drawn on the palm/fingers. The present invention is to detect and rectify such distortions using feedback paradigm. If image is of good quality, there is no need to renovate features. So, quality of whole image is checked by generating exponential similarity distribution. Quality of local region is checked by the ridge continuity map and ridge clarity map. Then, we check whether feedback is needed or not. The desired features such as ridge structure, minutiae point, orientation, etc. are renovated using feedback paradigm. Feedback is taken from top K matched template fingerprints registered in the database. Fuzzy logic handles uncertainties and imperfections in images. For matching, we have proposed the Enhanced Fuzzy Feature Match (EFFM) for estimating triangular feature set of distance between minutiae, orientation angle of minutiae, angle between the direction of minutiae points, angle between the interior bisector of triangle and the direction of minutiae, and a minutiae type. The proposed algorithm incorporates an additional parameter minutiae type that assists to improve accuracy of matching algorithm. The experimentation on 300 Mehndi fingerprints acquired using Secugen fingerprint scanner is conducted. The results positively support EEFM for its efficiency and reliability to handle distorted fingerprints matching
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