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Yes / Biometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method. / Horizon-MSCA-RISE-2019-2023, Marie Sklodowska-Curie

The overall aim of this research project has been to develop a reference dataset of 19 Holocene silicic Icelandic tephra layers sourced from the Torfajökull, Askja, Katla, Öræfajökull and Hekla volcanic systems. The dataset comprises geochemical data (including major, trace and rare earth element data for bulk and glass phases collected by XRF, electron microprobe, ion probe and laser ablation ICP-MS) and physical data (including sedimentary logs, field photographs, distribution maps and GPS localities of reference sections). Results indicate that Icelandic volcanic systems show unique geochemical signatures which result from the systems proximity to the active rifting zone and the proposed upwelling mantle plume that underlies the island. Within individual volcanic systems, eruptions produce tephra with distinct geochemical characteristics, which allow for the independent confirmation of tephra identity. The identification and discrimination of tephra layers can in some cases be achieved using major element chemistry (e.g. Hekla, H1104 – H5) while other tephra layers can only be discriminated using trace element chemistry (e.g. Torfajökull, Landnám and Gràkolla). Certain tephra layers however show near-identical geochemistry and therefore discrimination is not possible (e.g. Hekla, HA, HB, HC, HM, HN, HX, HY, HZ) without the incorporation of other proxy data. Icelandic micro-tephra horizons are identified in soil, lacustrine and marine sedimentary sequences and are used for dating and correlation in Quaternary studies. Data collected for this project will facilitate reliable data comparison and tephra identification between proximal and distal localities across the North Atlantic region. The data may also contribute to the debate regarding the formation of silicic rocks within Iceland, particularly with regard to the Hekla central volcano. The geochemical data collected for this thesis shows distinct age-dependant geochemical sub-groups suggesting temporal sub-surface relocation of the Hekla magma source.

Zugl.: Bonn, Univ., Diss., 2009

A novel algorithm for fingerprint template formation and matching in automatic fingerprint recognition has been developed. At present, fingerprint is being considered as the dominant biometric trait among all other biometrics due to its wide range of applications in security and access control. Most of the commercially established systems use singularity point (SP) or ‘core’ point for fingerprint indexing and template formation. The efficiency of these systems heavily relies on the detection of the core and the quality of the image itself. The number of multiple SPs or absence of ‘core’ on the image can cause some anomalies in the formation of the template and may result in high False Acceptance Rate (FAR) or False Rejection Rate (FRR). Also the loss of actual minutiae or appearance of new or spurious minutiae in the scanned image can contribute to the error in the matching process. A more sophisticated algorithm is therefore necessary in the formation and matching of templates in order to achieve low FAR and FRR and to make the identification more accurate. The novel algorithm presented here does not rely on any ‘core’ or SP thus makes the structure invariant with respect to global rotation and translation. Moreover, it does not need orientation of the minutiae points on which most of the established algorithm are based. The matching methodology is based on the local features of each minutiae point such as distances to its nearest neighbours and their internal angle. Using a publicly available fingerprint database, the algorithm has been evaluated and compared with other benchmark algorithms. It has been found that the algorithm has performed better compared to others and has been able to achieve an error equal rate of 3.5%.

This thesis deals with the issue of reconstruction of damaged fingerprints using artificial neural networks. At first, the fingerprint structure is analyzed, after that, the methods that can be used to improve fingerprint quality are described. An introduction to neural networks is given for understanding the basics of artificial neural networks. After choosing the right architecture for the neural networks, the process of its learning is described. A simple graphic user interface was created for this application, which is able to reconstruct synthetic fingerprints damaged by various warts. Another neural net can detect the location of wart. Tests have proven an increase in the quality of fingerprint by 43,5 % in the dataset with ten inserted warts on each fingerprint. The matching score was increased by 6,5 % on this particular dataset.

This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fin gerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale.

The goal of this master thesis is to design and implement an approach for synthetic fingerprint generation from a biometric template. The thesis bases the solution on an existing fingerprint generator called SyFDaS developed at the Brno University of Technology, Faculty of Information Technology. Individual components of the generator had to be modified and automized to suit better the task of generating from a template. The end product enables the user to create a fingerprint without any intervention just by importing a template. The evaluation in this thesis presents results obtained by comparing the synthetic and original fingerprints using the VeriFinger algorithm. Entirely automatically created fingerprints achieved mixed results; however, manual adjustments of the parameters brought substantial improvements. Up to 72% of synthetic fingerprints reached the match by the VeriFinger. The results of the evaluation helped to identify weak points of the current solution. Based on these, the thesis proposes further steps to improve the success rate of automatic generation and the quality of other components.

Liveness detection in automatic fingerprint identification systems (AFIS) is an issue which still prevents its use in many unsupervised security applications. In the last decade, various hardware and software solutions for the detection of liveness from fingerprints have been proposed by academic research groups. However, the proposed methods have not yet been practically implemented with existing AFIS. A large amount of research is needed before commercial AFIS can be implemented. In this research, novel active pore based liveness detection methods were proposed for AFIS. These novel methods are based on the detection of active pores on fingertip ridges, and the measurement of ionic activity in the sweat fluid that appears at the openings of active pores. The literature is critically reviewed in terms of liveness detection issues. Existing fingerprint technology, and hardware and software solutions proposed for liveness detection are also examined. A comparative study has been completed on the commercially and specifically collected fingerprint databases, and it was concluded that images in these datasets do not contained any visible evidence of liveness. They were used to test various algorithms developed for liveness detection; however, to implement proper liveness detection in fingerprint systems a new database with fine details of fingertips is needed. Therefore a new high resolution Brunel Fingerprint Biometric Database (B-FBDB) was captured and collected for this novel liveness detection research. The first proposed novel liveness detection method is a High Pass Correlation Filtering Algorithm (HCFA). This image processing algorithm has been developed in Matlab and tested on B-FBDB dataset images. The results of the HCFA algorithm have proved the idea behind the research, as they successfully demonstrated the clear possibility of liveness detection by active pore detection from high resolution images. The second novel liveness detection method is based on the experimental evidence. This method explains liveness detection by measuring the ionic activities above the sample of ionic sweat fluid. A Micro Needle Electrode (MNE) based setup was used in this experiment to measure the ionic activities. In results, 5.9 pC to 6.5 pC charges were detected with ten NME positions (50μm to 360 μm) above the surface of ionic sweat fluid. These measurements are also a proof of liveness from active fingertip pores, and this technique can be used in the future to implement liveness detection solutions. The interaction of NME and ionic fluid was modelled in COMSOL multiphysics, and the effect of electric field variations on NME was recorded at 5μm -360μm positions above the ionic fluid.

Fingerprints in blood indicate a threshold of violence has been surpassed in crime scenarios - making the crime resolution more urgent. There exist multiple processes that enhance a blood fingerprint in its original position, or in-situ, with reliability so that an image can be obtained. However, blood fingerprint evidence that cannot directly be transported to a laboratory for further analysis, due to the size or mobility of the substrate, calls for portability. In 2010 Zar-Pro Fluorescent Blood Lifting Strips were patented by Jessica Zarate as a "fluorogenic method for lifting, enhancing, and preserving blood impression evidence". The lifted prints are also inherently fluorescent to further increase enhancement and contrast of the print. There are currently no studies comparing Zar-Pro results with the results of other laboratory enhancement methods. This experiment compared Zar-Pro to other non-portable and frequently used alternatives - blood peak absorption and Hungarian Red enhancement to determine if Zar-Pro gives better blood fingerprint enhancement results than other non-portable alternatives - ALS visualization and Hungarian Red enhancement. In this study, Zar-Pro methods produced more reliable and reproducible results over the Hungarian Red and blood peak absorption methods on white and black ceramic tile. From this study, one can also conclude that ALS peak absorption is better suited for the location of blood prints on a light-colored item of evidence, rather than an enhancement method of blood prints.