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People can visualize their spontaneous and voluntary emotions via facial expressions, which play a critical role in social interactions. However, less is known about mechanisms of spontaneous emotion expressions, especially in adults with visual impairment and blindness. Nineteen adults with visual impairment and blindness participated in interviews where the spontaneous facial expressions were observed and analyzed via the Facial Action Coding System (FACS). We found a set of Action Units, primarily engaged in expressing the spontaneous emotions, which were likely to be affected by participants’ different characteristics. The results of this study could serve as evidence to suggest that adults with visual impairment and blindness show individual differences in spontaneous facial expressions of emotions.
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A Comparative Analysis of Emotion-Detecting AI Systems with Respect to Algorithm Performance and Dataset Diversity
In recent news, organizations have been considering the use of facial and emotion recognition for applications involving youth such as tackling surveillance and security in schools. However, the majority of efforts on facial emotion recognition research have focused on adults. Children, particularly in their early years, have been shown to express emotions quite differently than adults. Thus, before such algorithms are deployed in environments that impact the wellbeing and circumstance of youth, a careful examination should be made on their accuracy with respect to appropriateness for this target demographic. In this work, we utilize several datasets that contain facial expressions of children linked to their emotional state to evaluate eight different commercial emotion classification systems. We compare the ground truth labels provided by the respective datasets to the labels given with the highest confidence by the classification systems and assess the results in terms of matching score (TPR), positive predictive value, and failure to compute rate. Overall results show that the emotion recognition systems displayed subpar performance on the datasets of children's expressions compared to prior work with adult datasets and initial human ratings. We then identify limitations associated with automated recognition of emotions in children and provide suggestions on directions with enhancing recognition accuracy through data diversification, dataset accountability, and algorithmic regulation.
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- Award ID(s):
- 1849101
- PAR ID:
- 10109151
- Date Published:
- Journal Name:
- Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
- Page Range / eLocation ID:
- 377 to 382
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3306618.3314284
