A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490

An experimental study on the deformation behaviour and fracture mode of recycled aluminium alloy AA6061-reinforced alumina oxide undergoing high-velocity impact

The anisotropic behaviour and the damage evolution of recycled aluminium alloy-reinforced alumina oxide are investigated in this paper using Taylor impact test. The test is performed at various impact velocity ranging from 190 to 360 m/s by firing a cylindrical projectile towards anvil target. The deformation behaviour and the fracture modes are analysed using the digitized footprint of the deformed specimens. The damage initiation and the progression are observed around the impact surface and the surface 0.5 cm from the impact area using the scanning electron microscope. The deformed specimens showed several ductile fracture modes of mushrooming, tensile splitting and petalling. The critical impact velocity is defined below 280 m/s. The specimens showed a strong strain-rate dependency due to the damage evolution that is driven by severe localized plastic-strain deformation. The scanning electron microscope analysis showed the damage mechanism progress via voids initiation, growth and coalescence in the material. The micrograph within the footprint surface shows the presence of alumina oxide particles within the specimen. The microstructure analysis shows a significant refinement of the specimen particle at the surface located 0.5 cm above the impact area. ImageJ software is adopted in this work to measure the average size of voids within this surface. Non-symmetrical (ellipse-shaped) footprint around the footprints showed plastic anisotropic behaviour. The results in this paper provide a better understanding of the deformation behaviour of recycled materials subjected to dynamic loading. This information on mechanical response is crucial before any potential application can be established to substitute the primary sources

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)

Fast fingerprint verification using sub-regions of fingerprint images.

Chan Ka Cheong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Introduction to Fingerprint Verification --- p.1Chapter 1.1.1 --- Biometrics --- p.1Chapter 1.1.2 --- Fingerprint History --- p.2Chapter 1.1.3 --- Fingerprint characteristics --- p.4Chapter 1.1.4 --- A Generic Fingerprint Matching System Architecture --- p.6Chapter 1.1.5 --- Fingerprint Verification and Identification --- p.8Chapter 1.1.7 --- Biometric metrics --- p.10Chapter 1.2 --- Embedded system --- p.12Chapter 1.2.1 --- Introduction to embedded systems --- p.12Chapter 1.2.2 --- Embedded systems characteristics --- p.12Chapter 1.2.3 --- Performance evaluation of a StrongARM processor --- p.13Chapter 1.3 --- Objective -An embedded fingerprint verification system --- p.16Chapter 1.4 --- Organization of the Thesis --- p.17Chapter 2 --- Literature Reviews --- p.18Chapter 2.1 --- Fingerprint matching overviews --- p.18Chapter 2.1.1 --- Minutiae-based fingerprint matching --- p.20Chapter 2.2 --- Fingerprint image enhancement --- p.21Chapter 2.3 --- Orientation field Computation --- p.22Chapter 2.4 --- Fingerprint Segmentation --- p.24Chapter 2.5 --- Singularity Detection --- p.25Chapter 2.6 --- Fingerprint Classification --- p.27Chapter 2.7 --- Minutia extraction --- p.30Chapter 2.7.1 --- Binarization and thinning --- p.30Chapter 2.7.2 --- Direct gray scale approach --- p.32Chapter 2.7.3 --- Comparison of the minutiae extraction approaches --- p.35Chapter 2.8 --- Minutiae matching --- p.37Chapter 2.8.1 --- Point matching --- p.37Chapter 2.8.2 --- Structural matching technique --- p.38Chapter 2.9 --- Summary --- p.40Chapter 3. --- Implementation --- p.41Chapter 3.1 --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.1.1 --- Typical Fingerprint Matching System --- p.41Chapter 3.1.2. --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.2 --- Orientation computation --- p.43Chapter 3.21 --- Orientation computation --- p.43Chapter 3.22 --- Smooth orientation field --- p.43Chapter 3.3 --- Fingerprint image segmentation --- p.45Chapter 3.4 --- Reference Point Extraction --- p.46Chapter 3.5 --- A Classification Scheme --- p.51Chapter 3.6 --- Finding A Small Fingerprint Matching Area --- p.54Chapter 3.7 --- Fingerprint Matching --- p.57Chapter 3.8 --- Minutiae extraction --- p.59Chapter 3.8.1 --- Ridge tracing --- p.59Chapter 3.8.2 --- cross sectioning --- p.60Chapter 3.8.3 --- local maximum determination --- p.61Chapter 3.8.4 --- Ridge tracing marking --- p.62Chapter 3.8.5 --- Ridge tracing stop criteria --- p.63Chapter 3.9 --- Optimization technique --- p.65Chapter 3.10 --- Summary --- p.66Chapter 4. --- Experimental results --- p.67Chapter 4.1 --- Experimental setup --- p.67Chapter 4.2 --- Fingerprint database --- p.67Chapter 4.3 --- Reference point accuracy --- p.67Chapter 4.4 --- Variable number of matching minutiae results --- p.68Chapter 4.5 --- Contribution of the verification prototype --- p.72Chapter 5. --- Conclusion and Future Research --- p.74Chapter 5.1 --- Conclusion --- p.74Chapter 5.2 --- Future Research --- p.74Bibliography --- p.7

Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization

The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc. In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P

Fingerprint classification with combined neural networks

Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.EThOS - Electronic Theses Online ServiceGBUnited Kingdo