Iris recognition refers to the automated process of recognizing individuals based on their iris patterns. The seemingly stochastic nature of the iris stroma makes it a distinctive cue for biometric recognition. The textural nuances of an individual's iris pattern can be effectively extracted and encoded by projecting them onto Gabor wavelets and transforming the ensuing phasor response into a binary code - a technique pioneered by Daugman. This textural descriptor has been observed to be a robust feature descriptor with very low false match rates and low computational complexity. However, recent advancements in deep learning and computer vision indicate that generic descriptors extracted using convolutional neural networks (CNNs) are able to represent complex image characteristics. Given the superior performance of CNNs on the ImageNet large scale visual recognition challenge and a large number of other computer vision tasks, in this paper, we explore the performance of state-of-the-art pre-trained CNNs on iris recognition. We show that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-IrisThousand. We also discuss the challenges and future research directions in leveraging deep learning methods for the problem of iris recognition.

In this survey, we provide a comprehensive review of more than 200 articles, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition.

The incorporation of physical information in machine learning frameworks is opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work, we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of more than 250 papers on formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches are analyzed in terms of modeling and formulation of governing physical processes, including modifying input data (observation bias), network architectures (inductive bias), and training losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency, and generalization in increasingly realistic applications.

Increasingly people, especially those residing in urban areas with the urban heat island effect, are getting exposed to extreme heat due to ongoing global warming. A number of methods have been developed, so far, to assess urban heat vulnerability in different locations across the world concentrating on diverse aspects of these methods. While there is growing literature, thorough review studies that compare, contrast, and help understand the prospects and constraints of urban heat vulnerability assessment methods are scarce. This paper aims to bridge this gap in the literature. A systematic literature review with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach is utilized as the methodological approach. PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. The results are analyzed in three aspects—i.e., indicators and data, modelling approaches, and validation approaches. The main findings disclose that: (a) Three types of indicators are commonly used—i.e., demographic properties and socioeconomic status, health conditions and medical resources, and natural and built environmental factors; (b) Heat vulnerability indexing models, equal weighting method, and principal component analysis are commonly used in modelling and weighting approaches; (c) Statistical regressions and correlation coefficients between heat vulnerability results and adverse health outcomes are commonly used in validation approaches, but the performance varies across studies. This study informs urban policy and generates directions for prospective research and more accurate vulnerability assessment method development.