Bibliography | Reutter, Robin: Correlating facial expressions and contextual data for mood prediction using mobile devices. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 72 (2017). 91 pages, english.
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Abstract | Facial recognition can nowadays be achieved by any casual smartphone with a camera. Sophisticated systems and methods allow extracting information from facial data such as connected emotions depending on a person’s current expressions. In a mobile setting, information about emotions can be constantly grasped from a person using the smartphone front camera and annotated with various types of contextual data. This master thesis introduces OpenFaceAndroid - an Android application based on the existing facial analysis frameworks OpenFace and OpenFace++. The system allows to gather and process facial expression information as well as contextual data in real-time on a smartphone using the front camera device and various sensors. The output is a prediction of seven different emotions which is - compared to pure facial data extraction - improved through annotation with context. In two conducted studies first off data from several participants is collected, assessed for their usefulness in terms of this master thesis and afterward utilized to learn classifier models taking live emotion values and context information as training data. Subsequently, these models are evaluated for their accuracy in general emotion prediction and noticing affective mood changes - supported by findings of participant interviews. In conclusion, possible improvements and general limitations of this work are discussed as well as suggestions for future work proposed.
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Full text and other links | Volltext
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Department(s) | University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
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Superviser(s) | Schmidt, Prof. Albrecht; Kosch, Thomas; Hassib, Mariam |
Entry date | May 29, 2019 |
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