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.

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