Minutiae Extraktion från Fingeravtryck med Neural Nät och Minutiae baserad Fingeravtryck Verifikation

Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this thesis is to develop a complete system for fingerprint verification through extracting and matching minutiae. A neural network is trained using the back-propagation algorithm and will work as a classifier to locate various minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of binarization and skeletonization is first applied on fingerprints before they are evaluated by the neural network. Extracted minutiae are then considered as a 2D point pattern problem and an algorithm is used to determine the number of matching points between two point patterns. Performance of the developed system is evaluated on a database with fingerprints from different people and experimental results are presented.Människans fingeravtryck är rikt på detaljerna som kallas minutiae, vilka kan användas som identifierings märke för fingeravtryck verifikation. Mål med denna magister examen arbete är att ta fram ett komplett system för fingeravtryck verifikation igenom att extrahera och matcha minutiae. Ett neuralt nät blev tränat med hjälp av back-propagation algoritm och kommer att arbeta som en klassificerare för att lokalisera de olika minutiae. För att få en bra minutiae extraktion i fingeravtryck med varierande kvalitet behövs det en förbehandling i form av binarization och skelettifiering som appliceras på fingeravtryck innan de är evaluerade av neurala nätet. Extraherade minutiae är betraktade som en 2D punkt mönster problem och en algoritm används för att fastställa antal matchande punkter mellan två punkt mönster. Systemets prestanda testas på en databas med fingeravtryck från tjugo personer och den experimentella resultat presenteras.Adress: Josef Ström Bartunek Studentvägen 1:40 372 40 Ronneby tel.nr.:073-612 75 3

FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION

Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations. This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems. In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis

Minutiae Extraktion från Fingeravtryck med Neural Nät och Minutiae baserad Fingeravtryck Verifikation

Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this thesis is to develop a complete system for fingerprint verification through extracting and matching minutiae. A neural network is trained using the back-propagation algorithm and will work as a classifier to locate various minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of binarization and skeletonization is first applied on fingerprints before they are evaluated by the neural network. Extracted minutiae are then considered as a 2D point pattern problem and an algorithm is used to determine the number of matching points between two point patterns. Performance of the developed system is evaluated on a database with fingerprints from different people and experimental results are presented.Människans fingeravtryck är rikt på detaljerna som kallas minutiae, vilka kan användas som identifierings märke för fingeravtryck verifikation. Mål med denna magister examen arbete är att ta fram ett komplett system för fingeravtryck verifikation igenom att extrahera och matcha minutiae. Ett neuralt nät blev tränat med hjälp av back-propagation algoritm och kommer att arbeta som en klassificerare för att lokalisera de olika minutiae. För att få en bra minutiae extraktion i fingeravtryck med varierande kvalitet behövs det en förbehandling i form av binarization och skelettifiering som appliceras på fingeravtryck innan de är evaluerade av neurala nätet. Extraherade minutiae är betraktade som en 2D punkt mönster problem och en algoritm används för att fastställa antal matchande punkter mellan två punkt mönster. Systemets prestanda testas på en databas med fingeravtryck från tjugo personer och den experimentella resultat presenteras.Adress: Josef Ström Bartunek Studentvägen 1:40 372 40 Ronneby tel.nr.:073-612 75 3

FINGERPRINT IMAGE ENHANCEMENT, SEGMENTATION AND MINUTIAE DETECTION

Prior to 1960's, the fingerprint analysis was carried out manually by human experts and for forensic purposes only. Automated fingerprint identification systems (AFIS) have been developed during the last 50 years. The success of AFIS resulted in that its use expanded beyond forensic applications and became common also in civilian applications. Mobile phones and computers equipped with fingerprint sensing devices for fingerprint-based user identification are common today. Despite the intense development efforts, a major problem in automatic fingerprint identification is to acquire reliable matching features from fingerprint images with poor quality. Images where the fingerprint pattern is heavily degraded usually inhibit the performance of an AFIS system. The performance of AFIS systems is also reduced when matching fingerprints of individuals with large age variations. This doctoral thesis presents contributions within the field of fingerprint image enhancement, segmentation and minutiae detection. The reliability of the extracted fingerprint features is highly dependent on the quality of the obtained fingerprints. Unfortunately, it is not always possible to have access to high quality fingerprints. Therefore, prior to the feature extraction, an enhancement of the quality of fingerprints and a segmentation are performed. The segmentation separates the fingerprint pattern from the background and thus limits possible sources of error due to, for instance, feature outliers. Most enhancement and segmentation techniques are data-driven and therefore based on certain features extracted from the low quality fingerprints at hand. Hence, different types of processing, such as directional filtering, are employed for the enhancement. This thesis contributes by proposing new research both for improving fingerprint matching and for the required pre-processing that improves the extraction of features to be used in fingerprint matching systems. In particular, the majority of enhancement and segmentation methods proposed herein are adaptive to the characteristics of each fingerprint image. Thus, the methods are insensitive towards sensor and fingerprint variability. Furthermore, introduction of the higher order statistics (kurtosis) for fingerprint segmentation is presented. Segmentation of the fingerprint image reduces the computational load by excluding background regions of the fingerprint image from being further processed. Also using a neural network to obtain a more robust minutiae detector with a patch rejection mechanism for speeding up the minutiae detection is presented in this thesis

Analysis of Periodicities in Surface Topography of Cold rolled sheets Using Data Captured by Camera System

A method for surface analysis of cold rolled sheets is proposed in this paper. The approach is based on a low-cost specially built camera system followed by spectral analysis of the data captured from metal surfaces. The focus is on the changes in the surface topography caused by cold rolling with emphasis towards periodicities in the processed surface. Angular profile of the spectrum is calculated and used to display periodicities in surface topography and show their direction. The results obtained by using the proposed system were compared with results obtained from the optical profilometer MicroProf FRT. The experiments show that cold rolling creates marks on the surface of the material, which represent periodicities that can be effectively detected by the proposed method and camera system. Even though the camera system is not able to measure precise surface roughness, it is able to detect periodicities and the results of spectral analysis are comparable with the results from the optical profilometer.open access</p

Neural Network based Minutiae Extraction from Skeletonized Fingerprints

Human fingerprints are rich in details denoted minutiae. In this paper a method of minutiae extraction from fingerprint skeletons is described. To identify the different shapes and types of minutiae a neural network is trained to work as a classifier. The proposed neural network is applied throughout the fingerprint skeleton to locate various minutiae. A scheme to speed up the process is also presented. Extracted minutiae can then be used as identification marks for automatic fingerprint matching

Improved Adaptive Fingerprint Binarization

In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented

Adaptive Fingerprint Binarization by Frequency Domain Analysis

This paper presents a new approach for fingerprint enhancement by using directional filters and binarization. A straightforward method for automatically tuning the size of local area is obtained by analyzing entire fingerprint image in the frequency domain. Hence, the algorithm will adjust adaptively to the local area of the fingerprint image, independent on the characteristics of the fingerprint sensor or the physical appearance of the fingerprints. Frequency analysis is carried out in the local areas to design directional filters. Experimental results are presented

Implementation and evaluation of NIST Biometric Image Software for fingerprint recognition

Fingerprints are rich in details which are in the form of discontinuities in ridges known as minutiae and are unique for each person. This paper describes implementation and evaluation of an existing fingerprint recognition system in MATLAB environment. The selected system is developed by National Institute of Standards and Technology (NIST) denoted as Biometric Image Software (NBIS). The NBIS source code is written in ANSI C programming language. To be able to evaluate the algorithm in MATLAB a C language MEX-files has been used. The NBIS support both minutiae extraction and minutiae matching functions that have been employed in the evaluation. The implemented system has been tested on a Fingerprint Verification Competition (FVC) database. The results are presented as Receiver Operating Characteristics (ROC) graphs

Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data

This article proposes several improvements to an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Five processing blocks comprise the adaptive fingerprint enhancement method, where four of these blocks are updated in our proposed system. Hence, the proposed overall system is novel. The four updated processing blocks are: 1) preprocessing; 2) global analysis; 3) local analysis; and 4) matched filtering. In the preprocessing and local analysis blocks, a nonlinear dynamic range adjustment method is used. In the global analysis and matched filtering blocks, different forms of order statistical filters are applied. These processing blocks yield an improved and new adaptive fingerprint image processing method. The performance of the updated processing blocks is presented in the evaluation part of this paper. The algorithm is evaluated toward the NIST developed NBIS software for fingerprint recognition on FVC databases