Risk analysis in biometric-based Border Inspection System

The main goal of a Border Inspection System is to prevent the entry of individuals who pose a threat to a country. The entry of just one of these persons could have severe consequences. Nevertheless, performing a lengthy border inspection is not possible, given that 240,737 international passengers enter the country in an average day [5]. For this reason, the primary inspection is performed using biometrics traits and information flow processes that have a low false acceptance rate and have a high throughput.;This thesis uses the analytic modeling tool called LQNS (Layered Queueing Network Solver) to solve open models for biometric-based border inspection system and cost curves to evaluate the risk. The contributions of the thesis include a performance model of a biometric-based border inspection using open workloads and a risk model of a biometric-based border inspection using cost curves. Further, we propose an original methodology for analyzing a combination of performance risk and security risk in the border inspection system

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification. Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts. However, since the latent fingerprints are accidentally leftover on different surfaces, the lifted prints look inferior. Therefore, a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance. As a result, there is an ever-growing demand to develop reliable and robust systems. In this regard, we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition, segmentation, quality assessment, enhancement, feature extraction, and matching steps. Later, we provide insight into different benchmark latent datasets available to perform research in this area. Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation, enhancement, extraction, and matching approaches to strengthen the research

The New Employment Verification Act: The Functionality and Constitutionality of Biometrics in the Hiring Process Note

In 1990, Congress created the U.S. Commission on Immigration Reform to assess and make recommendations regarding the implementation and impact of U.S. immigration policy. Unanimously, the Commission proposed employment-based immigration reforms that have lead to the creation of E-Verify, an Internet-based electronic verification system used by employers to verify a prospective worker’s eligibility. Today, the system compares a prospective worker’s identification information, such as her name, date of birth, and social security number with information contained in databases housed by the Department of Homeland Security and Social Security Administration. Several members of Congress, however, have proposed legislation that would require prospective workers to submit biometric information to curb identity fraud and existing shortfalls in the verification process. This Note examines the practical and legal implications of a nationally mandated biometric verification system and whether such a system is constitutionally viable under current Fourth Amendment jurisprudence. Ultimately this Note argues that no matter how unsettling the collection of biometric information by the government may be, at least in the employment hiring context, a nationally mandated biometric verification system will most likely pass constitutional muster

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

Two Dimensional Clipping Based Segmentation Algorithm for Grayscale Fingerprint Images

One of the huge methods in Automated Fingerprint Identification System (AFIS) is the segment or separation of the fingerprint. The process of decomposing an image into exclusive components is referred as segmentation. Fingerprint segmentation is the one of the predominant process involved in fingerprint pre-processing and it refers to the method of dividing or separating the image into disjoint areas as the foreground and the background region. The foreground also called as Region of Interest (ROI) due to the fact only the region which contains ridge and valley structure is used for processing, whilst the background carries noisy and irrelevant content material and so that it will be discarded in later enhancement or orientation or classification method. The challenge proper right here is to decide which a part of the image belongs to the foreground, retrieved as an input from the fingerprint sensor device or from benchmark datasets and which part belongs to the background. A 100% correct segmentation is continually very tough, specifically inside the very poor quality image or partial image together with the presence of latent. In this paper, we discuss a modified clipped based segmentation algorithm by adopting threshold value and canny edge detection techniques. We segment the background image is x and y dimensions or in other words left the edge, right edge, top edge and bottom edge of the image. For the purpose of analyzing the algorithm FVC ongoing 2002 benchmark dataset is considered. The entire algorithm is implemented using MATLAB 2015a. The algorithm is able to find affectively ROI of the fingerprint image or separates the foreground region from the background area of the fingerprint image very effectively. In high configuration system proposed algorithm achieves execution time of 1.75 seconds

An assessment of the usability of biometric signature systems using the human-biometric sensor interaction model’

Signature biometrics is a widely used form of user authentication. As a behavioural biometric, samples have inherent inconsistencies which must be accounted for within an automated system. Performance deterioration of a tuned biometric software system may be caused by an interaction error with a biometric capture device, however, using conventional error metrics, system and user interaction errors are combined, thereby masking the contribution by each element. In this paper we explore the application of the Human-Biometric Sensor Interaction (HBSI) model to signature as an exemplar of a behavioural biometric. Using observational data collected from a range of subjects, our study shows that usability issues can be identified specific to individual capture device technologies. While most interactions are successful, a range of common interaction errors need to be mitigated by design to reduce overall error rates

Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

The Internet of Things devices and users exchange massive amount of data. Some of these exchanged messages are highly sensitive as they involve organizational, military or patient personally identifiable information. Therefore, many schemes and protocols have been put forward to protect the transmitted messages. The techniques deployed in these schemes may include blockchain, public key infrastructure, elliptic curve cryptography, physically unclonable function and radio frequency identification. In this paper, a review is provided of these schemes including their strengths and weaknesses. Based on the obtained results, it is clear that majority of these protocols have numerous security, performance and privacy issues