Facing Employers and Customers

Muralidhar, S.; Siegfried, Rémy; Odobez, Jean-Marc; Gática-Pérez, Daniel

doi:10.1145/3282894.3282925

Cited by 11 publications

(2 citation statements)

References 34 publications

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“…For example, ML has been used for the assessment of candidates ( Faliagka et al, 2012 ) and identification of leadership roles ( Doornenbal et al, 2021 ) as well as personality traits of managers ( Hrazdil et al, 2020 ). Machine learning has also been used to study communication skills in the workplace ( Suen et al, 2020 ) and evaluate gaze patterns and facial expressions ( Muralidhar et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Virtual reality stimulation and organizational neuroscience for the assessment of empathy

et al. 2022

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This study aimed to evaluate the viability of a new procedure based on machine learning (ML), virtual reality (VR), and implicit measures to discriminate empathy. Specifically, eye-tracking and decision-making patterns were used to classify individuals according to their level in each of the empathy dimensions, while they were immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centered design approach. Interaction and gaze patterns were recorded for 82 participants, who were classified as having high or low empathy on each of the following empathy dimensions: perspective-taking, emotional understanding, empathetic stress, and empathetic joy. The dimensions were assessed using the Cognitive and Affective Empathy Test. An ML-based model that combined behavioral outputs and eye-gaze patterns was developed to predict the empathy dimension level of the participants (high or low). The analysis indicated that the different dimensions could be differentiated by eye-gaze patterns and behaviors during immersive VR. The eye-tracking measures contributed more significantly to this differentiation than did the behavioral metrics. In summary, this study illustrates the potential of a novel VR organizational environment coupled with ML to discriminate the empathy dimensions. However, the results should be interpreted with caution, as the small sample does not allow general conclusions to be drawn. Further studies with a larger sample are required to support the results obtained in this study.

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Section: Introductionmentioning

confidence: 99%

Virtual reality stimulation and organizational neuroscience for the assessment of empathy

et al. 2022

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“…By providing access to the attention of people or even to their intention and mind, gaze tracking finds application in many domains ranging from human behavior and communication analysis [Ba and Odobez 2008], human-robot/computer interaction [Klotz et al 2011], psychological studies [Muralidhar et al 2018], or medical diagnosis [Isaac et al 2014]. However, beyond the sheer instantaneous estimation of gaze direction, gaze analytics can often benefit from the recognition of the actual eye movements (saccades, blinks, ...).…”

Section: Introductionmentioning

confidence: 99%

A deep learning approach for robust head pose independent eye movements recognition from videos

Siegfried

Odobez

2019

Proceedings of the 11th ACM Symposium on Eye Tracking Research &Amp; Applications

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Recognizing eye movements is important for gaze behavior understanding like in human communication analysis (human-human or robot interactions) or for diagnosis (medical, reading impairments). In this paper, we address this task using remote RGB-D sensors to analyze people behaving in natural conditions. This is very challenging given that such sensors have a normal sampling rate of 30 Hz and provide low-resolution eye images (typically 36x60 pixels), and natural scenarios introduce many variabilities in illumination, shadows, head pose, and dynamics. Hence gaze signals one can extract in these conditions have lower precision compared to dedicated IR eye trackers, rendering previous methods less appropriate for the task. To tackle these challenges, we propose a deep learning method that directly processes the eye image video streams to classify them into fixation, saccade, and blink classes, and allows to distinguish irrelevant noise (illumination, low-resolution artifact, inaccurate eye alignment, difficult eye shapes) from true eye motion signals. Experiments on natural 4-party interactions demonstrate the benefit of our approach compared to previous methods, including deep learning models applied to gaze outputs. CCS CONCEPTS • Computing methodologies → Tracking; Neural networks; Activity recognition and understanding.

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Judging an Airbnb booking by its cover: how profile photos affect guest ratings

Jang

2022

JCM

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Purpose This research aims to examine whether the facial appearances and expressions of Airbnb host photos influence guest star ratings. Design/methodology/approach This research analyzed the profile photos of over 20,000 Airbnb hosts and the guest star ratings of over 30,000 Airbnb listings in New York City, using machine learning techniques. Findings First, hosts who provided profile photos received higher guest ratings than those who did not provide photos. When facial features of profile photos were recognizable, guest ratings were higher than when they were not recognizable (e.g. faces too small, faces looking backward or faces blocked by some objects). Second, a happy facial expression, blond hair and brown hair positively affected guest ratings, whereas heads tilted back negatively affected guest ratings. Originality/value This research is the first, to the best of the authors’ knowledge, to analyze the facial appearances and expressions of profile photos using machine learning techniques and examine the influence of Airbnb host photos on guest star ratings.

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Facing Employers and Customers

Cited by 11 publications

References 34 publications

Virtual reality stimulation and organizational neuroscience for the assessment of empathy

Virtual reality stimulation and organizational neuroscience for the assessment of empathy

A deep learning approach for robust head pose independent eye movements recognition from videos

Judging an Airbnb booking by its cover: how profile photos affect guest ratings

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