A Longitudinal Analysis on the Feasibility of Iris Recognition Performance for Infants 0-2 Years Old

The focus of this study was to longitudinally evaluate iris recognition for infants between the ages of 0 to 2 years old. Image quality metrics of infant and adult irises acquired on the same iris camera were compared. Matching performance was evaluated for four groups, infants 0 to 6 months, 7 to 12 months, 13 to 24 months, and adults. A mixed linear regression model was used to determine if infants’ genuine similarity scores changed over time. This study found that image quality metrics were different between infants and adults but in the older group, (13 to 24 months old) the image quality metric scores were more likely to be similar to adults. Infants 0 to 6 months old had worse performance at an FMR of 0.01% than infants 7 to 12 months, 13 to 24 months, and adults

FACIAL IDENTIFICATION FOR DIGITAL FORENSIC

Forensic facial recognition has become an essential requirement in criminal investigations as a result of the emergence of electronic devices, such as mobile phones and computers, and the huge volume of existing content. Forensic facial recognition goes beyond facial recognition in that it deals with facial images under unconstrained and non-ideal conditions, such as low image resolution, varying facial orientation, poor illumination, a wide range of facial expressions, and the presence of accessories. In addition, digital forensic challenges do not only concern identifying an individual but also include understanding the context, acknowledging the relationships between individuals, tracking, and numbers of advanced questions that help reduce the cognitive load placed on the investigator. This thesis proposes a multi-algorithmic fusion approach by using multiple commercial facial recognition systems to overcome particular weaknesses in singular approaches to obtain improved facial identification accuracy. The advantage of focusing on commercial systems is that they release the forensic team from developing and managing their own solutions and, subsequently, also benefit from state-of-the-art updates in underlying recognition performance. A set of experiments was conducted to evaluate these commercial facial recognition systems (Neurotechnology, Microsoft, and Amazon Rekognition) to determine their individual performance using facial images with varied conditions and to determine the benefits of fusion. Two challenging facial datasets were identified for the evaluation; they represent a challenging yet realistic set of digital forensics scenarios collected from publicly available photographs. The experimental results have proven that using the developed fusion approach achieves a better facial vi identification rate as the best evaluated commercial system has achieved an accuracy of 67.23% while the multi-algorithmic fusion system has achieved an accuracy of 71.6%. Building on these results, a novel architecture is proposed to support the forensic investigation concerning the automatic facial recognition called Facial-Forensic Analysis System (F-FAS). The F-FAS is an efficient design that analyses the content of photo evidence to identify a criminal individual. Further, the F-FAS architecture provides a wide range of capabilities that will allow investigators to perform in-depth analysis that can lead to a case solution. Also, it allows investigators to find answers about different questions, such as individual identification, and identify associations between artefacts (facial social network) and presents them in a usable and visual form (geolocation) to draw a wider picture of a crime. This tool has also been designed based on a case management concept that helps to manage the overall system and provide robust authentication, authorisation, and chain of custody. Several experts in the forensic area evaluated the contributions of theses and a novel approach idea and it was unanimously agreed that the selected research problem was one of great validity. In addition, all experts have demonstrated support for experiments’ results and they were impressed by the suggested F-FAS based on the context of its functions.Republic of Iraq / Ministry of Higher Education and Scientific Research – Baghdad Universit

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Biometric ID Cybersurveillance

The implementation of a universal digitalized biometric ID system risks normalizing and integrating mass cybersurveillance into the daily lives of ordinary citizens. ID documents such as driver’s licenses in some states and all U.S. passports are now implanted with radio frequency identification (RFID) technology. In recent proposals, Congress has considered implementing a digitalized biometric identification card—such as a biometric-based, “high-tech” Social Security Card—which may eventually lead to the development of a universal multimodal biometric database (e.g., the collection of the digital photos, fingerprints, iris scans, and/or DNA of all citizens and noncitizens). Such “hightech” IDs, once merged with GPS-RFID tracking technology, would facilitate exponentially a convergence of cybersurveillance-body tracking and data surveillance, or dataveillance-biographical tracking. Yet, the existing Fourth Amendment jurisprudence is tethered to a “reasonable expectation of privacy” test that does not appear to restrain the comprehensive, suspicionless amassing of databases that concern the biometric data, movements, activities, and other personally identifiable information of individuals. In this Article, I initiate a project to explore the constitutional and other legal consequences of big data cybersurveillance generally and mass biometric dataveillance in particular. This Article focuses on how biometric data is increasingly incorporated into identity management systems through bureaucratized cybersurveillance or the normalization of cybersurveillance through the daily course of business and integrated forms of governance

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

BioTwist - overcoming severe distortions in ridge-based biometrics for successful identication

Biometrics rely on a physical trait's permanence and stability over time, as well as its individuality, robustness and ease to be captured. Challenges arise when working with newborns or infants because of the tininess and fragility of an infant's features, their uncooperative nature and their rapid growth. The last of these is particularly relevant when one tries to verify an infant's identity based on captures of a biometric taken at an earlier age. Finding a physical trait that is feasible for infants is often referred to as the infant biometric problem. This thesis explores the quality aspect of adult fingermarks and the correlation between image quality and the mark’s usefulness for an ongoing forensic investigation, and researches various aspects of the “ballprint” as an infant biometric. The ballprint, the friction ridge skin area of the foot pad under the big toe, exhibits similar properties to fingerprint but the ball possesses larger physical structures and a greater number of features. We collected a longitudinal ballprint database from 54 infants within 3 days of birth, at two months old, at 6 months and at 2 years. It has been observed that the skin of a newborn's foot dries and cracks so the ridge lines are often not visible to the naked eye and an adult fingerprint scanner cannot capture them. This thesis presents the physiological discovery that the ballprint grows isotropically during infancy and can be well approximated by a linear function of the infant's age. Fingerprint technology developed for adult fingerprints can match ballprints if they are adjusted by a physical feature (the inter-ridge spacing) to be of a similar size to adult fingerprints. The growth in ballprint inter-ridge spacing mirrors infant growth in terms of length/height. When growth is compensated for by isotropic rescaling, impressive verification scores are achieved even for captures taken 22 months apart. The scores improve even further when low-quality prints are rejected; the removal of the bottom third improves the Equal Error Rate from 1-2% to 0%. In conclusion, this thesis demonstrates that the ballprint is a feasible solution to the infant biometric problem