Das Ziel der vorliegenden Forschungsarbeit besteht darin, leistungsfähige State-of-the-Art-Architekturen im Bereich der Emotionserkennung aufzuzeigen, sie in ihre Komponenten zu zerlegen, eingehend zu analysieren und durch eigene Implementierung zu testen.
Die vorgestellten Verfahren sollen anhand verschiedener Datensätze mit unterschiedlichen Bewertungsmaßstäben evaluiert werden, um das normative Bewertungsverfahren im Bereich des maschinellen Lernens zu erweitern. Darüber hin-aus wird eine Webanwendung implementiert, in der die Modelle in Echtzeit erprobt werden können. Dabei spielt nicht nur die Leistungsfähigkeit und Genauigkeit der Modelle eine Rolle, sondern auch ihre Funktionsfähigkeit im Hinblick auf die Echtzeitverarbeitung in einer Produktionsumgebung.
Der Einsatz von Technologie in der Gesellschaft hat in den letzten Jahrzehnten drastisch zugenommen. Heute werden Maschinen in vielen verschiedenen Branchen verwendet. Je mehr sie mit dem Menschen interagieren, desto reibungsloser und natürlicher muss die Interaktion ausgestaltet sein. Um dies zu erreichen, müssen die Maschinen mit einer Fähigkeit ausgestattet werden, die es ihnen ermöglicht, ihre Umgebung zu verstehen. Zu diesem Zweck ist es sinnvoll, zunächst einen Blick auf die alltägliche zwischenmenschliche Kommunikation zu werfen, bei der akustische und visuelle Signale zu Kommunikationszwecken genutzt werden. Bei den visuellen Signalen liefert die Mimik zahl-reiche Informationen über die tatsächlichen Gefühle und Absichten einer Person. Laut einer Studie von Albert Mehrabian sind Mimik und Gestik in der zwischenmenschlichen Kommunikation von großer Bedeutung. Es wurde die Theorie aufgestellt, dass die Kommunikation zu 7 Prozent verbal, zu 38 Prozent vokal und zu 55 Prozent visuell erfolgt. Das Ergebnis war, dass 93 Prozent der Kommunikation nonverbaler Natur seien. Ferner ist anzumerken, dass das Defizit im Sozialverhalten mit der Schwierigkeit korreliert, Emotionen in der Mimik anderer zu erkennen.
- Quote paper
- Aziz Togru (Author), 2022, Echtzeit-Erkennung von Emotionen durch neuronale Netze und Machine Learning. Evaluation verschiedener Verfahren, Munich, GRIN Verlag, https://www.grin.com/document/1407459
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