Determination of vitality from a non-invasive biomedical measurement for use in integrated biometric devices

Personal identification is a very important issue in today\u27s mobile and electronically networked societies. Among the available measures, fingerprints are the oldest and most widely used. Unfortunately, it is usually possible to deceive automatic fingerprint identification systems by presenting a well-duplicated synthetic or dismembered finger. This project is one method to provide fingerprint vitality authentication in order to solve this problem. Using a sensor that is composed of an array of capacitors, this method identifies the vitality of a fingerprint by detecting a specific changing pattern over the human skin. Mapping the two-dimensional images into one-dimensional signals, two ensembles of measures, namely static and dynamic measures, are used for classification. Static patterns as well as temporal changes in dielectric mosaic structure of the skin demonstrate themselves in these signals. Using these measures, this algorithm quantifies this specific pattern and makes a final decision about vitality of the fingerprint by a neural network trained by examples

Using biometrics authentication via fingerprint recognition in e-Exams in e-Learning environment

E-learning is a great opportunity for modern life. Notably, however, the tool needs to be coupled with efficient and reliable security mechanisms to ensure the medium can be established as a dependable one. Authentication of e-exam takers is of prime importance so that exams are given by fair means. A new approach shall be proposed so as to ensure that no unauthorised individuals are permitted to give the exams

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks

Textural features for fingerprint liveness detection

The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets

Skin Tissue Terahertz Imaging for Fingerprint Biometrics

abstract: Fingerprints have been widely used as a practical method of biometrics authentication or identification with a significant level of security. However, several spoofing methods have been used in the last few years to bypass fingerprint scanners, thus compromising data security. The most common attacks occur by the use of fake fingerprint during image capturing. Imposters can build a fake fingerprint from a latent fingerprint left on items such as glasses, doorknobs, glossy paper, etc. Current mobile fingerprint scanning technology is incapable of differentiating real from artificial fingers made from gelatin molds and other materials. In this work, the adequacy of terahertz imaging was studied as an alternative fingerprint scanning technique that will enhance biometrics security by identifying superficial skin traits. Terahertz waves (0.1 – 10 THz) are a non-ionizing radiation with significant penetration depth in several non-metallic materials. Several finger skin features, such as valley depth and sweat ducts, can possibly be imaged by employing the necessary imaging topology. As such, two imaging approaches 1) using quasi-optical components and 2) using near-field probing were investigated. The numerical study is accomplished using a commercial Finite Element Method tool (ANSYS, HFSS) and several laboratory experiments are conducted to evaluate the imaging performance of the topologies. The study has shown that terahertz waves can provide high spatial resolution images of the skin undulations (valleys and ridges) and under certain conditions identify the sweat duct pattern.Dissertation/ThesisMasters Thesis Electrical Engineering 201

On the design of forgiving biometric security systems

This work aims to highlight the fundamental issue surrounding biometric security systems: it's all very nice until a biometric is forged, but what do we do after that? Granted, biometric systems are by physical nature supposedly much harder to forge than other factors of authentication since biometrics on a human body are by right unique to the particular human person. Yet it is also due to this physical nature that makes it much more catastrophic when a forgery does occur, because it implies that this uniqueness has been forged as well, threatening the human individuality; and since crime has by convention relied on identifying suspects by biometric characteristics, loss of this biometric uniqueness has devastating consequences on the freedom and basic human rights of the victimized individual. This uniqueness forgery implication also raises the motivation on the adversary to forge since a successful forgery leads to much more impersonation situations when biometric systems are used i.e. physical presence at crime scenes, identi cation and access to security systems and premises, access to nancial accounts and hence the ability to use the victim's nances. Depending on the gains, a desperate highly motivated adversary may even resort to directly obtaining the victim's biometric parts by force e.g. severing the parts from the victim's body; this poses a risk and threat not just to the individual's uniqueness claim but also to personal safety and well being. One may then wonder if it is worth putting one's assets, property and safety into the hands of biometrics based systems when the consequences of biometric forgery far outweigh the consequences of system compromises when no biometrics are used

Biologically inspired evolutionary temporal neural circuits

Biological neural networks have always motivated creation of new artificial neural networks, and in this case a new autonomous temporal neural network system. Among the more challenging problems of temporal neural networks are the design and incorporation of short and long-term memories as well as the choice of network topology and training mechanism. In general, delayed copies of network signals can form short-term memory (STM), providing a limited temporal history of events similar to FIR filters, whereas the synaptic connection strengths as well as delayed feedback loops (ER circuits) can constitute longer-term memories (LTM). This dissertation introduces a new general evolutionary temporal neural network framework (GETnet) through automatic design of arbitrary neural networks with STM and LTM. GETnet is a step towards realization of general intelligent systems that need minimum or no human intervention and can be applied to a broad range of problems. GETnet utilizes nonlinear moving average/autoregressive nodes and sub-circuits that are trained by enhanced gradient descent and evolutionary search in terms of architecture, synaptic delay, and synaptic weight spaces. The mixture of Lamarckian and Darwinian evolutionary mechanisms facilitates the Baldwin effect and speeds up the hybrid training. The ability to evolve arbitrary adaptive time-delay connections enables GETnet to find novel answers to many classification and system identification tasks expressed in the general form of desired multidimensional input and output signals. Simulations using Mackey-Glass chaotic time series and fingerprint perspiration-induced temporal variations are given to demonstrate the above stated capabilities of GETnet

Novel active sweat pores based liveness detection techniques for fingerprint biometrics

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.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.This study is funded by the University of Sindh, Jamshoro, Pakistan and the Higher Education Commission of Pakistan