Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed-accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed-accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their related advantages and disadvantages. The review also attempts to incorporate publications on a broader range of collocation-based physics informed neural networks, which stars form the vanilla PINN, as well as many other variants, such as physics-constrained neural networks (PCNN), variational hp-VPINN, and conservative PINN (CPINN). The study indicates that most research has focused on customizing the PINN through different activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the wide range of applications for which PINNs have been used, by demonstrating their ability to be more feasible in some contexts than classical numerical techniques like Finite Element Method (FEM), advancements are still possible, most notably theoretical issues that remain unresolved. 

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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda

A systematic review of IoT in healthcare: Applications, techniques, and trends

Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.

Face detection techniques: a review

The Internet of Things (IoT) enabled various types of applications in the field of information technology, smart and connected health care is notably a crucial one is one of them. Our physical and mental health information can be used to bring about a positive transformation change in the health care landscape using networked sensors. It makes it possible for monitoring to come to the people who don't have ready access to effective health monitoring system. The captured data can then be analyzed using various machine learning algorithms and then shared through wireless connectivity with medical professionals who can make appropriate recommendations. These scenarios already exist, but we intend to enhance it by analyzing the past data for predicting future problems using prescriptive analytics. It will allow us to move from reactive to visionary approach by rapidly spotting trends and making recommendations on behalf of the actual medical service provider. In this paper, the authors have applied different machine learning techniques and considered public datasets of health care stored in the cloud to build a system, which allows for real time and remote health monitoring built on IoT infrastructure and associated with cloud computing. The system will be allowed to drive recommendations based on the historic and empirical data lying on the cloud. The authors have proposed a framework to uncover knowledge in a database, bringing light to disguise patterns which can help in credible decision making. This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease. Experimental results are conducted using a few machine learning algorithms considered in this paper like K-NN, Support Vector Machine, Decision Trees, Random Forest, and MLP.

A healthcare monitoring system using random forest and internet of things (IoT)

Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks.

A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities

There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.

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Munish Kumar

Papers

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Face detection techniques: a review

A healthcare monitoring system using random forest and internet of things (IoT)

A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities

Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment.