Everyday, people flexibly perform different categorizations of common faces, objects and scenes. Intuition and scattered evidence suggest that these categorizations require the use of different visual information from the input. However, there is no unifying method, based on the categorization performance of subjects, that can isolate the information used. To this end, we developed Bubbles, a general technique that can assign the credit of human categorization performance to specific visual information. To illustrate the technique, we applied Bubbles on three categorization tasks (gender, expressive or not and identity) on the same set of faces, with human and ideal observers to compare the features they used.

Bubbles: A Technique to Reveal the Use of Information in Recognition Tasks EOA Report

The increasing prevalence of infectious diseases in recent decades has posed a serious threat to public health. Routes of transmission differ, but the respiratory droplet or airborne route has the greatest potential to disrupt social intercourse, while being amenable to prevention by the humble face mask. Different types of masks give different levels of protection to the user. The ongoing COVID-19 pandemic has even resulted in a global shortage of face masks and the raw materials that go into them, driving individuals to self-produce masks from household items. At the same time, research has been accelerated towards improving the quality and performance of face masks, e.g., by introducing properties such as antimicrobial activity and superhydrophobicity. This review will cover mask-wearing from the public health perspective, the technical details of commercial and home-made masks, and recent advances in mask engineering, disinfection, and materials and discuss the sustainability of mask-wearing and mask production into the future.

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Face Masks in the New COVID-19 Normal: Materials, Testing, and Perspectives.

The identification of emotional expressions is vital for social interaction, and can be affected by various factors, including the expressed emotion, the intensity of the expression, the sex of the face, and the gender of the observer. This study investigates how these factors affect the speed and accuracy of expression recognition, as well as dwell time on the two most significant areas of the face: the eyes and the mouth. Participants were asked to identify expressions from female and male faces displaying six expressions (anger, disgust, fear, happiness, sadness, and surprise), each with three levels of intensity (low, moderate, and normal). Overall, responses were fastest and most accurate for happy expressions, but slowest and least accurate for fearful expressions. More intense expressions were also classified most accurately. Reaction time showed a different pattern, with slowest response times recorded for expressions of moderate intensity. Overall, responses were slowest, but also most accurate, for female faces. Relative to male observers, women showed greater accuracy and speed when recognizing female expressions. Dwell time analyses revealed that attention to the eyes was about three times greater than on the mouth, with fearful eyes in particular attracting longer dwell times. The mouth region was attended to the most for fearful, angry, and disgusted expressions and least for surprise. These results extend upon previous findings to show important effects of expression, emotion intensity, and sex on expression recognition and gaze behaviour, and may have implications for understanding the ways in which emotion recognition abilities break down.

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Identification of Emotional Facial Expressions: Effects of Expression, Intensity, and Sex on Eye Gaze

Covid-19 pandemics has fostered a pervasive use of facemasks all around the world. While they help in preventing infection, there are concerns related to the possible impact of facemasks on social communication. The present study investigates how emotion recognition, trust attribution and re-identification of faces differ when faces are seen without mask, with a standard medical facemask, and with a transparent facemask restoring visual access to the mouth region. Our results show that, in contrast to standard medical facemasks, transparent masks significantly spare the capability to recognize emotional expressions. Moreover, transparent masks spare the capability to infer trustworthiness from faces with respect to standard medical facemasks which, in turn, dampen the perceived untrustworthiness of faces. Remarkably, while transparent masks (unlike standard masks) do not impair emotion recognition and trust attribution, they seemingly do impair the subsequent re-identification of the same, unmasked, face (like standard masks). Taken together, this evidence supports a dissociation between mechanisms sustaining emotion and identity processing. This study represents a pivotal step in the much-needed analysis of face reading when the lower portion of the face is occluded by a facemask.

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The impact of facemasks on emotion recognition, trust attribution and re-identification

The human face is central to our everyday social interactions. Recent studies have shown that while gazing at faces, each one of us has a particular eye-scanning pattern, highly stable across time. Although variables such as culture or personality have been shown to modulate gaze behavior, we still don't know what shapes these idiosyncrasies. Moreover, most previous observations rely on static analyses of small-sized eye-position data sets averaged across time. Here, we probe the temporal dynamics of gaze to explore what information can be extracted about the observers and what is being observed. Controlling for any stimuli effect, we demonstrate that among many individual characteristics, the gender of both the participant (gazer) and the person being observed (actor) are the factors that most influence gaze patterns during face exploration. We record and exploit the largest set of eye-tracking data (405 participants, 58 nationalities) from participants watching videos of another person. Using novel data-mining techniques, we show that female gazers follow a much more exploratory scanning strategy than males. Moreover, female gazers watching female actresses look more at the eye on the left side. These results have strong implications in every field using gaze-based models from computer vision to clinical psychology.

Face exploration dynamics differentiate men and women.

When distinguishing whether a face displays a certain emotion, some regions of the face may contain more useful information than others. Here we ask whether people differentially attend to distinct regions of a face when judging different emotions. Experiment 1 measured eye movements while participants discriminated between emotional (joy, anger, fear, sadness, shame, and disgust) and neutral facial expressions. Participant eye movements primarily fell in five distinct regions (eyes, upper nose, lower nose, upper lip, nasion). Distinct fixation patterns emerged for each emotion, such as a focus on the lips for joyful faces and a focus on the eyes for sad faces. These patterns were strongest for emotional faces but were still present when viewers sought evidence of emotion within neutral faces, indicating a goal-driven influence on eye-gaze patterns. Experiment 2 verified that these fixation patterns tended to reflect attention to the most diagnostic regions of the face for each emotion. Eye movements appear to follow both stimulus-driven and goal-driven perceptual strategies when decoding emotional information from a face.

J. Nelson

Papers

Eye movements during emotion recognition in faces.