Inferring User Facial Affect in Work-like Settings

• URL: http://arxiv.org/abs/2111.11862v1
• Date: Mon, 22 Nov 2021 01:23:46 GMT
• Title: Inferring User Facial Affect in Work-like Settings
• Authors: Chaudhary Muhammad Aqdus Ilyas, Siyang Song, Hatice Gunes
• Abstract summary: We aim to infer user facial affect when the user is engaged in multiple work-like tasks under varying difficulty levels. We first design a study with different conditions and gather multimodal data from 12 subjects. We then perform several experiments with various machine learning models and find that the display and prediction of facial affect vary from non-working to working settings.
• Score: 5.630425653717262
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unlike the six basic emotions of happiness, sadness, fear, anger, disgust and surprise, modelling and predicting dimensional affect in terms of valence (positivity - negativity) and arousal (intensity) has proven to be more flexible, applicable and useful for naturalistic and real-world settings. In this paper, we aim to infer user facial affect when the user is engaged in multiple work-like tasks under varying difficulty levels (baseline, easy, hard and stressful conditions), including (i) an office-like setting where they undertake a task that is less physically demanding but requires greater mental strain; (ii) an assembly-line-like setting that requires the usage of fine motor skills; and (iii) an office-like setting representing teleworking and teleconferencing. In line with this aim, we first design a study with different conditions and gather multimodal data from 12 subjects. We then perform several experiments with various machine learning models and find that: (i) the display and prediction of facial affect vary from non-working to working settings; (ii) prediction capability can be boosted by using datasets captured in a work-like context; and (iii) segment-level (spectral representation) information is crucial in improving the facial affect prediction.

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