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 at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

Do Alzheimer's Disease Patients Appear Younger than Their Real Age?

INTRODUCTION: The most prominent risk factor of Alzheimer's disease (AD) is aging. Aging also influences the physical appearance. Our clinical experience suggests that patients with AD may appear younger than their actual age. Based on this empirical observation, we set forth to test the hypothesis with human and computer-based estimation systems. METHOD: We compared 50 early-stage AD patients with 50 age and sex-matched controls. Facial images of all subjects were recorded using a video camera with high resolution, frontal view, and clear lighting. Subjects were recorded during natural conversations while performing Mini-Mental State Examination, including spontaneous smiles in addition to static images. The images were used for age estimation by 2 methods: (1) computer-based age estimation; (2) human-based age estimation. Computer-based system used a state-of-the-art deep convolutional neural network classifier to process the facial images contained in a single-video session and performed frame-based age estimation. Individuals who estimated the age by visual inspection of video sequences were chosen following a pilot selection phase. The mean error (ME) of estimations was the main end point of this study. RESULTS: There was no statistically significant difference between the ME scores for AD patients and healthy controls (p = 0.33); however, the difference was in favor of younger estimation of the AD group. The average ME score for AD patients was lower than that for healthy controls in computer-based estimation system, indicating that AD patients were on average estimated to be younger than their actual age as compared to controls. This difference was statistically significant (p = 0.007). CONCLUSION: There was a tendency for humans to estimate AD patients younger, and computer-based estimations showed that AD patients were estimated to be younger than their real age as compared to controls. The underlying mechanisms for this observation are unclear.

Analyzing international airtime top-up transfers for migration and mobility.

International airtime top-up transfers enable prepaid mobile phone users to send top-ups and data bundles to users in other countries, as well as make payments, in real time. These are heavily used by migrants to financially assist their families in their home countries and consequently could be a valuable source of information for migration and mobility analysis. However, top-up transfers are understudied as a form of money remittance in migration. In this paper, we explore the determinants and the potential of top-up transactions to complement remittance and migration statistics. Our results show that such data can provide insights into migrant groups, particularly for irregular migration and for estimating the real-time distribution of migrant groups for a given country.

T-Patterns revisited: mining for temporal patterns in sensor data.

The trend to use large amounts of simple sensors as opposed to a few complex sensors to monitor places and systems creates a need for temporal pattern mining algorithms to work on such data. The methods that try to discover re-usable and interpretable patterns in temporal event data have several shortcomings. We contrast several recent approaches to the problem, and extend the T-Pattern algorithm, which was previously applied for detection of sequential patterns in behavioural sciences. The temporal complexity of the T-pattern approach is prohibitive in the scenarios we consider. We remedy this with a statistical model to obtain a fast and robust algorithm to find patterns in temporal data. We test our algorithm on a recent database collected with passive infrared sensors with millions of events.

Joint attention by gaze interpolation and saliency.

Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.

A statistical method for 2-D facial landmarking.

Many facial-analysis approaches rely on robust and accurate automatic facial landmarking to correctly function. In this paper, we describe a statistical method for automatic facial-landmark localization. Our landmarking relies on a parsimonious mixture model of Gabor wavelet features, computed in coarse-to-fine fashion and complemented with a shape prior. We assess the accuracy and the robustness of the proposed approach in extensive cross-database conditions conducted on four face data sets (Face Recognition Grand Challenge, Cohn-Kanade, Bosphorus, and BioID). Our method has 99.33% accuracy on the Bosphorus database and 97.62% accuracy on the BioID database on the average, which improves the state of the art. We show that the method is not significantly affected by low-resolution images, small rotations, facial expressions, and natural occlusions such as beard and mustache. We further test the goodness of the landmarks in a facial expression recognition application and report landmarking-induced improvement over baseline on two separate databases for video-based expression recognition (Cohn-Kanade and BU-4DFE).