Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications.Biometric, Fingerprints, Distortion, Fuzzy Clustering, Cost Sensitive Classifier

Distorted Fingerprint Verification System

Fingerprint verification is one of the most reliable personal identification methods. Fingerprint matching is affected by non-linear distortion introduced in fingerprint impression during the image acquisition process. This non-linear deformation changes both the position and orientation of minutiae. The proposed system operates in three stages: alignment based fingerprint matching, fuzzy clustering and classifier framework. First, an enhanced input fingerprint image has been aligned with the template fingerprint image and matching score is computed. To improve the performance of the system, a fuzzy clustering based on distance and density has been used to cluster the feature set obtained from the fingerprint matcher. Finally a classifier framework has been developed and found that cost sensitive classifier produces better results. The system has been evaluated on fingerprint database and the experimental result shows that system produces a verification rate of 96%. This system plays an important role in forensic and civilian applications

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

Low-Quality Fingerprint Classification

Traditsioonilised sõrmejälgede tuvastamise süsteemid kasutavad otsuste tegemisel minutiae punktide informatsiooni. Nagu selgub paljude varasemate tööde põhjal, ei ole sõrmejälgede pildid mitte alati piisava kvaliteediga, et neid saaks kasutada automaatsetes sõrmejäljetuvastuse süsteemides. Selle takistuse ületamiseks keskendub magistritöö väga madala kvaliteediga sõrmejälgede piltide tuvastusele – sellistel piltidel on mitmed üldteada moonutused, nagu kuivus, märgus, füüsiline vigastatus, punktide olemasolu ja hägusus. Töö eesmärk on välja töötada efektiivne ja kõrge täpsusega sügaval närvivõrgul põhinev algoritm, mis tunneb sõrmejälje ära selliselt madala kvaliteediga pildilt. Eksperimentaalsed katsed sügavõppepõhise meetodiga näitavad kõrget tulemuslikkust ja robustsust, olles rakendatud praktikast kogutud madala kvaliteediga sõrmejälgede andmebaasil. VGG16 baseeruv sügavõppe närvivõrk saavutas kõrgeima tulemuslikkuse kuivade (93%) ja madalaima tulemuslikkuse häguste (84%) piltide klassifitseerimisel.Fingerprint recognition systems mainly use minutiae points information. As shown in many previous research works, fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this challenge, in this thesis, we are focusing on very low-quality fingerprint images, which contain several well-known distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. We develop an efficient, with high accuracy, deep neural network algorithm, which recognizes such low-quality fingerprints. The experimental results have been conducted on real low-quality fingerprint database, and the achieved results show the high performance and robustness of the introduced deep network technique. The VGG16 based deep network achieves the highest performance of 93% for dry and the lowest of 84% for blurred fingerprint classes

A new enhancement of fingerprint classification for the damaged fingerprint with adaptive features

In this paper, we propose an new enhancement of the classification for damaged fingerprint database.It is based on the fact that damaged fingerprint image is composed of regular texture regions that can be successfully represents by co-occurrence matrices.So, we first extract the features based on certain characteristics and then we use these features to train a neural network for classifying fingerprints into five classes.The obtained results compared with existing approaches demonstrate the superior performance of our new enhancement

Fingerprint Recognition in Biometric Security -A State of the Art

Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud. so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it\'s fashionable due to their easy accessibility. during this paper we tend to discuss the elaborated study of various gift implementation define strategies together with their comparative measures and result analysis thus as realize a brand new constructive technique for fingerprint recognition

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 Orientation Refinement Through Iterative Smoothing

We propose a new gradient-based method for the extraction of the orientation field associated to a fingerprint, and a regularisation procedure to improve the orientation field computed from noisy fingerprint images. The regularisation algorithm is based on three new integral operators, introduced and discussed in this paper. A pre-processing technique is also proposed to achieve better performances of the algorithm. The results of a numerical experiment are reported to give an evidence of the efficiency of the proposed algorithm

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book