A Survey on Computer Vision based Human Analysis in the COVID-19 Era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure

Face Liveness Detection Using a 2D Camera

Rozpoznávanie tváre je jednou z najviac spoločensky akceptovaných foriem biometrického rozpoznávania. Nedávna dostupnosť veľmi presných a efektívnych algoritmov rozpoznávania tváre ponecháva zraniteľnosť voči prezentačným útokom ako hlavnú výzvu pre riešenia rozpoznávania tváre. Táto práca sa zaoberá vysvetlením problematiky týkajúcej sa s detekciou živosti tváre, ktorá pomôže pochopiť rôzne možnosti útokov a ich vzťah k existujúcim riešeniam. A implementáciu algoritmu, ktorý na základe videa rozoznáva živosť tváre.Facial recognition is one of the most socially accepted forms of biometric recognition. The recent availability of highly accurate and efficient face recognition algorithms leaves vulnerability to presentation attacks as a major challenge for face recognition solutions. This work deals with the explanation of the issues related to the detection of facial liveliness, which will help to understand the various possibilities of attack and their relationship to existing solutions. And the implementation of an algorithm that recognizes the liveliness of the face based on videos.

Image Classification of High Variant Objects in Fast Industrial Applications

Recent advances in machine learning and image processing have expanded the applications of computer vision in many industries. In industrial applications, image classification is a crucial task since high variant objects present difficult problems because of their variety and constant change in attributes. Computer vision algorithms can function effectively in complex environments, working alongside human operators to enhance efficiency and data accuracy. However, there are still many industries facing difficulties with automation that have not yet been properly solved and put into practice. They have the need for more accurate, convenient, and faster methods. These solutions drove my interest in combining multiple learning strategies as well as sensors and image formats to enable the use of computer vision for these applications. The motivation for this work is to answer a number of research questions that aim to mitigate current problems in hinder their practical application. This work therefore aims to present solutions that contribute to enabling these solutions. I demonstrate why standard methods cannot simply be applied to an existing problem. Each method must be customized to the specific application scenario in order to obtain a working solution. One example is face recognition where the classification performance is crucial for the system’s ability to correctly identify individuals. Additional features would allow higher accuracy, robustness, safety, and make presentation attacks more difficult. The detection of attempted attacks is critical for the acceptance of such systems and significantly impacts the applicability of biometrics. Another application is tailgating detection at automated entrance gates. Especially in high security environments it is important to prevent that authorized persons can take an unauthorized person into the secured area. There is a plethora of technology that seem potentially suitable but there are several practical factors to consider that increase or decrease applicability depending which method is used. The third application covered in this thesis is the classification of textiles when they are not spread out. Finding certain properties on them is complex, as these properties might be inside a fold, or differ in appearance because of shadows and position. The first part of this work provides in-depth analysis of the three individual applications, including background information that is needed to understand the research topic and its proposed solutions. It includes the state of the art in the area for all researched applications. In the second part of this work, methods are presented to facilitate or enable the industrial applicability of the presented applications. New image databases are initially presented for all three application areas. In the case of biometrics, three methods that identify and improve specific performance parameters are shown. It will be shown how melanin face pigmentation (MFP) features can be extracted and used for classification in face recognition and PAD applications. In the entrance control application, the focus is on the sensor information with six methods being presented in detail. This includes the use of thermal images to detect humans based on their body heat, depth images in form of RGB-D images and 2D image series, as well as data of a floor mounted sensor-grid. For textile defect detection several methods and a novel classification procedure, in free-fall is presented. In summary, this work examines computer vision applications for their practical industrial applicability and presents solutions to mitigate the identified problems. In contrast to previous work, the proposed approaches are (a) effective in improving classification performance (b) fast in execution and (c) easily integrated into existing processes and equipment