Analysis and detection of fingerprint creases

Fingerprint is a biometric trait that is widely used for human identification and verification. Most fingerprint biometric systems make use of certain salient features on the fingerprint, including minutiae points, pores, and singular points, for comparing two fingerprint images. In this work, we explore the possibility of using fingerprint creases for comparing two fingerprints. Creases can be described as white lines or scars on a fingerprint image. Recent studies have determined that some creases are genetically influenced although the origin of creases has not been completely characterized. While no published work exists for crease matching, some studies have explored the problem of automated crease detection. In this thesis, we study the possibility of using creases for fingerprint matching. We also suggest two techniques to automatically extract creases from an input fingerprint image. Finally, we study the correlation between fingerprint creases and age of an individual

Multimodal Sensor Data Integration for Indoor Positioning in Ambient-Assisted Living Environments

A reliable Indoor Positioning System (IPS) is a crucial part of the Ambient-Assisted Living (AAL) concept. The use of Wi-Fi fingerprinting techniques to determine the location of the user, based on the Received Signal Strength Indication (RSSI) mapping, avoids the need to deploy a dedicated positioning infrastructure but comes with its own issues. Heterogeneity of devices and RSSI variability in space and time due to environment changing conditions pose a challenge to positioning systems based on this technique. The primary purpose of this research is to examine the viability of leveraging other sensors in aiding the positioning system to provide more accurate predictions. In particular, the experiments presented in this work show that Inertial Motion Units (IMU), which are present by default in smart devices such as smartphones or smartwatches, can increase the performance of Indoor Positioning Systems in AAL environments. Furthermore, this paper assesses a set of techniques to predict the future performance of the positioning system based on the training data, as well as complementary strategies such as data scaling and the use of consecutive Wi-Fi scanning to further improve the reliability of the IPS predictions. This research shows that a robust positioning estimation can be derived from such strategies

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

GNSS-free outdoor localization techniques for resource-constrained IoT architectures : a literature review

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

A literature analysis examining the potential suitability of terahertz imaging to detect friction ridge detail preserved in the imprimatura layer of oil-based, painted artwork

This literature analysis examines terahertz (THz) imaging as a non-invasive tool for the imaging of friction ridge detail from the first painted layer (imprimatura) in multilayered painted works of art. The paintings of interest are those created utilizing techniques developed during the Renaissance and still in use today. The goal of analysis serves to answer two questions. First, can THz radiation penetrate paint layers covering the imprimatura to reveal friction ridge information? Secondly, can the this technology recover friction ridge detail such that the fine details are sufficiently resolved to provide images suitable for comparison and identification purposes. If a comparison standard exists, recovered friction ridge detail from this layer can be used to establish linkages to an artist or between works of art. Further, it can be added to other scientific methods currently employed to assist with the authentication efforts of unattributed paintings. Flanked by the microwave and far-infrared edges, THz straddles the electronic and optic perspectives of the electromagnetic spectrum. As a consequence, this range is imparted with unique and useful properties. Able to penetrate and image through many opaque materials, its non-ionizing radiation is an ideal non-destructive technique that provides visual information from a painting’s sub-strata. Imaging is possible where refractive index differences exist between different paint layers. Though it is impossible, at present, to determine when a fingerprint was deposited, one can infer approximately when a print was created if it is recovered from the imprimatura layer of a painting, and can be subsequently attributed to a known source. Fingerprints are unique, a person is only able to deposit prints while their physical body is intact and thus, in some cases, the multiple layer process some artists use in their work may be used to the examiner’s advantage. Impressions of friction ridge detail have been recorded on receiving surfaces from human hands throughout time (and have also been discovered in works of art). Yet, the potential to associate those recorded impressions to a specific individual was only realized just over one hundred years ago. Much like the use of friction ridge skin, the relatively recently discovered THz range is now better understood; its tremendous potential unlocked by growing research and technology designed to exploit its unique properties

Defending Face-Recognition Technology (And Defending Against It)

This Article looks beneath the surface of attacks on face-recognition technology and explains how it can be an exceptionally useful tool for law enforcement, complementing traditional forensic evidence such as fingerprints and DNA. It punctures myths about the technology and explains how existing rules of criminal procedure, developed for other kinds of forensic evidence, are readily adaptable to face-recognition. It opposes across-the-board restrictions on use of face-recognition technologies and advocates a more sophisticated set of guarantees of defendant access to the information necessary to probe reliability of computerized face-matches. Defendants must have reasonable access to the details of the technology and how it was used so that they have a meaningful opportunity to inform the factfinder of doubts about reliability. Part II explains the technology, starting with machine learning, which enables a computer to represent faces digitally based on their physical characteristics, so that they can be matched with other faces. This part also explains how shortcomings in the algorithms or training database of faces can produce errors, both positive and negative, in identification. Part III explores existing and potential uses of face-recognition in law enforcement, placing the technology into the context of traditional police investigations. Part IV summarizes the relatively sparse caselaw and the much fuller literature on face-recognition technology, in particular, evaluates claims of threats to privacy, and analyzes legal principles developed for analogous conventional criminal investigative and proof methods. Part V constructs a legal framework for evaluating the probativeness of face-recognition technology in criminal prosecutions, develops strategies, and offers actual cross examination questions to guide defense counsel in challenging face-recognition technology. Part VI acknowledges that some specific uses of the technology to scan crowds and streams of people may need judicial control and suggests a draft statute to assure such control

The Potential of Using Brain Images for Authentication

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition