Kuster, D;
Steinert, L;
Baker, M;
Bhardwaj, N;
Krumhuber, EG;
(2022)
Teardrops on My Face: Automatic Weeping Detection from Nonverbal Behavior.
IEEE Transactions on Affective Computing
10.1109/TAFFC.2022.3228749.
(In press).
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Abstract
Human emotional tears are a powerful socio-emotional signal. Yet, they have received relatively little attention in empirical research compared to facial expressions or body posture. While humans are highly sensitive to others' tears, to date, no automatic means exist for detecting spontaneous weeping. This paper employed facial and postural features extracted using four pre-trained classifiers (FACET, Affdex, OpenFace, OpenPose) to train a Support Vector Machine (SVM) to distinguish spontaneous weepers from non-weepers. Results showed that weeping can be accurately inferred from nonverbal behavior. Importantly, this distinction can be made before the appearance of visible tears on the face. However, features from at least two classifiers need to be combined, with the best models blending three or four classifiers to achieve near-perfect performance (97% accuracy). We discuss how direct and indirect tear detection methods may help to yield important new insights into the antecedents and consequences of emotional tears and how affective computing could benefit from the ability to recognize and respond to this uniquely human signal.
Type: | Article |
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Title: | Teardrops on My Face: Automatic Weeping Detection from Nonverbal Behavior |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TAFFC.2022.3228749 |
Publisher version: | https://ieeexplore.ieee.org/document/9984983 |
Language: | English |
Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Face recognition, Feature extraction, Videos, Affective computing, Emotion recognition, Training data, Psychology |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10162563 |
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