Machine Learning for Biometrics

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems

Technoscience Art: A Bridge between Neuroesthetics and Art History?

One of the recent and exciting developments in mainstream art history is its confrontation with the cognitive sciences and neurology. This study is based on the problems these disciplines face before they can contribute to each other. We inspect several critical issues resulting from this encounter, especially in the context of the recently developing field of neuroesthetics. We argue that it is the language barrier between the disciplines, rather than any fundamental conceptual divison, that causes the lack of understanding on both sides. Shared terms in arts and neuroscience are elusive, and the different connotations of extant terms in these separate disciplines must be addressed. We propose technoscience art as a ground where joint terminology may be developed, an audience familiar to the concerns of both sides can be formed, and a new generation of scientifically-knowledgeable artists and scientists can interact for their mutual benefit

Registration of 3D Face Scans with Average Face Models

The accuracy of a 3D face recognition system depends on a correct registration that aligns the facial surfaces and makes a comparison possible. The best results obtained so far use a costly one-to-all registration approach, which requires the registration of each facial surface to all faces in the gallery. We explore the approach of registering the new facial surface to an average face model (AFM), which automatically establishes correspondence to the pre-registered gallery faces. We propose a new algorithm for constructing an AFM, and show that it works better than a recent approach. Extending the single-AFM approach, we propose to employ category-specific alternative AFMs for registration, and evaluate the effect on subsequent classification. We perform simulations with multiple AFMs that correspond to different clusters in the face shape space and compare these with gender and morphology based groupings. We show that the automatic clustering approach separates the faces into gender and morphology groups, consistent with the other race effect reported in the psychology literature. We inspect thin-plate spline and iterative closest point based registration schemes under manual or automatic landmark detection prior to registration. Finally, we describe and analyse a regular re-sampling method that significantly increases the accuracy of registration

3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions

This paper presents an evaluation of several 3D face recognizers on the Bosphorus database, which was gathered for studies on expression and pose invariant face analysis. We provide identification results of three 3D face recognition algorithms, namely generic face template based ICP approach, one-to-all ICP approach, and depth image-based Principal Component Analysis (PCA) method. All of these techniques treat faces globally and are usually accepted as baseline approaches. In addition, 2D texture classifiers are also incorporated in a fusion setting. Experimental results reveal that even though global shape classifiers achieve almost perfect identification in neutral-to-neutral comparisons, they are sub-optimal under extreme expression variations. We show that it is possible to boost the identification accuracy by focusing on the rigid facial regions and by fusing complementary information coming from shape and texture modalities

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Perceptual Fusion in Humans and Machines

Humans perceive the world through different perceptual modalities, which are processed in the brain by modality-specific areas and structures. However, there also exist multimodal neurons and areas, specialized in integrating perceptual information to enhance or suppress brain response. The particular way the human brain fuses crossmodal (or multimodal) perceptual information manifests itself first in behavioural studies. These crossmodal interactions are widely explored in some modalities, especially for auditory and visual input, and less explored for other modalities, like taste and olfaction, yet it is known that these effects can occur with any two modalities. The integration of sensory data is an important research area in computer science, and stands to benefit from the studies into the brain function; many biological processes serve as models for computer algorithms. On the other hand, computer models of sensor integration are built on mathematical principles, and provide normative insights into the functioning of the brain. This paper surveys the psychological and neurological findings pertaining to human multi-sensor fusion, followed by a brief review of the relevant computer science terminology and modeling approaches. The potential of an interdisciplinary approach to information fusion encompassing neuroscience, psychology and computer science have recently been recognized, and a multidisciplinary workshop on biologically inspired information fusion was organized to bring researchers together to determine a common agenda. The conclusion summarizes the agenda of research outlined at the workshop and attempts to raise research questions for the future

Retinotopy and Selective Visual Attention in Humans and Computers

Selective visual attention is a fascinating mechanism of the human brain that is essential to solve the difficult visual search problem. In this paper we first review the recent neurological findings that establish the presence and functional properties of detailed spatial representations beyond early visual cortex and investigate the role of attention on these retinotopic representations. Then we survey the computer vision literature to show how visual selective attention is incorporated into algorithms and models of visual search

Prediction and Voronoi graph construction with a dense network of simple sensors

It is possible to monitor an environment with a dense network of cheap and simple sensors instead of a few expensive and complex sensors. In this paper, we show how to use the T-pattern algorithm to make sense of a large number of simple sensor readings. We propose two modifications to the basic T-pattern algorithm to use it in this context. In particular, we predict sensor activations given present activity, and we derive a topological layout of the environment automatically