Investigation of the Role of Face Shape on the Flow Dynamics and Effectiveness of Face Masks

Due to the COVID-19 pandemic, face masks have been used extensively in society. The effectiveness of face masks depends on their material, design, and fit. With much research being focused on quantifying the role of the material, the design and fit of masks have been an afterthought at most. Recent studies, on the other hand, have shown that the mask fit is a significant factor to consider when specifying the effectiveness of the face mask. Moreover, the fit is highly dependent on face topology. Differences in face types and anthropometrics lead to different face mask fit. Here, computational fluid dynamics simulations employing a novel model for porous membranes (i.e., masks) are used to study the leakage pattern of a cough through a face mask on different faces. The three faces studied (female, male, and child) are characteristic faces identified in a previous population study. The female face is observed to have the most leakage through the periphery of the mask, which results in the lowest fitted filtration efficiency of the three faces. The male and child faces had similar gap profiles, leakage and fitted filtration efficiencies. However, the flow of the three faces differs significantly. The effect of the porosity of the mask was also studied. While all faces showed the same general trend with changing porosity, the effect on the child's face was more significant.

Bridge the gap: correlate face mask leakage and facial features with 3D morphable face models

<h4>Background</h4> Face masks have been proven to be effective in protecting the public against airborne transmitted diseases when fitted appropriately. However, for homemade cloth masks and surgical masks, the fit is often poor, allowing viruses to escape through the gap. <h4>Objective</h4> This work aims to identify the correlation between the mask leakage, mask configurations, and individual’s facial features. <h4>Methods</h4> A novel locally morphing 3D face model, and a minimum-energy-based mask deployment model are used to systematically examine the mask fit for a large cohort of exemplars. <h4>Results</h4> The results show that the mask size and tuck-in ratio, along with selective facial features, especially nose height and chin length, are key factors determining the leakage location and extent. A polynomial regression model is presented for mask fitness based on localized facial features. <h4>Significance</h4> This study is a complete pipeline to test various masks on a wide range of faces with controlled modification of distinct regions of the face, which is difficult to achieve with human subjects, and provide knowledge on how the masks should be designed in the future. <h4>Impact statement</h4> The face mask “fit” affects the mask’s efficacy in preventing airborne transmission. To date, research on the face mask fit has been conducted mainly using experiments on limited subjects. The limited sample size in experimental studies makes it hard to reach a statistical correlation between mask fit and facial features in a population. Here, we employ a novel framework that utilizes a morphable face model and mask’s deployment simulation to test mask fit for many facial characteristics and mask designs. The proposed technique is an important step toward enabling personalized mask selection with maximum efficacy for society members.

One size fits all?: A simulation framework for face-mask fit on population-based faces.

The use of face masks by the general population during viral outbreaks such as the COVID-19 pandemic, although at times controversial, has been effective in slowing down the spread of the virus. The extent to which face masks mitigate the transmission is highly dependent on how well the mask fits each individual. The fit of simple cloth masks on the face, as well as the resulting perimeter leakage and face mask efficacy, are expected to be highly dependent on the type of mask and facial topology. However, this effect has, to date, not been adequately examined and quantified. Here, we propose a framework to study the efficacy of different mask designs based on a quasi-static mechanical model of the deployment of face masks onto a wide range of faces. To illustrate the capabilities of the proposed framework, we explore a simple rectangular cloth mask on a large virtual population of subjects generated from a 3D morphable face model. The effect of weight, age, gender, and height on the mask fit is studied. The Centers for Disease Control and Prevention (CDC) recommended homemade cloth mask design was used as a basis for comparison and was found not to be the most effective design for all subjects. We highlight the importance of designing masks accounting for the widely varying population of faces. Metrics based on aerodynamic principles were used to determine that thin, feminine, and young faces were shown to benefit from mask sizes smaller than that recommended by the CDC. Besides mask size, side-edge tuck-in, or pleating, of the masks as a design parameter was also studied and found to have the potential to cause a larger localized gap opening.