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.

The performance of an iris recognition system depends greatly on how well the iris segmentation part of the system performs its task. The performance of an iris segmentation algorithm can be evaluated using different criteria and methods. Some of the methods evaluate the performance of the segmentation algorithm based on the performance of the whole iris recognition system. Other methods evaluate the performance of an iris segmentation subsystem independent of the performance of the system's other subsystems. To our knowledge there do not exist a generally accepted method or criteria for the evaluation of the standalone iris segmentation subsystem. This paper proposes a novel methodology to compare the performance of different iris segmentation algorithms, applied to different image datasets in a consistent way. The methodology employs the F1 score and an empirical cumulative distribution function. The implementation of the F1 score estimation, adapted to the iris segmentation task is described. Finally the application of the proposed methodology is demonstrated and discussed.

This paper proposes a new method for an automatic detection of a resolution of a scale or a ruler with graduation marks in the shoeprint images. The method creates a vector of the correlations estimated from the co-occurrence matrices for every row in a shoeprint image. The scale resolution is estimated from maxima in Fourier spectrum of the correlations’ vectors. The proposed method is evaluated on over 500 images taken at crime scenes and in a forensics laboratory. The experimental results indicate the possibility of applying the proposed method to automatically estimate the scale resolution in forensic images. The automatic detection of a scale resolution could be used to automatically rescale a forensic image before the printing this image in “one-to-one” scale. Furthermore, the proposed method could be used to automatically rescale images to an equal scale thus allowing to compare the images digitally. © 2017 Elsevier B.V.

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

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.

This paper presents a queue simulation of the VISIR Open Lab Platform. A model of the platform and statistical distributions of how users interact with the system based on real log files are presented. The system is then simulated in order to determine how many concurrent students that can be allowed to use the platform while at the same time keeping a low response time to ensure the quality of the service. The results show, in a worst case setup with approximately 300 ms response time per experiment, that roughly 100 concurrent users is an upper limit to ensure an average response time below 2 s. The results also show that raising the limit of the desired experiment response time does not necessarily increase the number allowed concurrent users significantly once the system is saturated. However, improving the experiment response time can significantly increase the number of users that can simultaneously be connected.

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.