Abstract
This disclosure describes techniques that capture the uncertainty in machine-vision based affect (emotion) perception. The techniques are capable of predicting aleatoric, epistemic, and annotation uncertainty. Measures of uncertainty are important to safety-critical and subjective assessment tasks such as those found in the perception of affective expressions.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Ghandeharioun, Asma; Jou, Brendan; Eoff, Brian; and Schroff, Florian, "Uncertainty modeling in affective computing", Technical Disclosure Commons, (April 09, 2019)
https://www.tdcommons.org/dpubs_series/2131
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