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The importance of considering individual differences in expression for in-vehicle emotion recognition - a machine learning approach

Le Houcq Corbi, Raquel und Ihme, Klas und Drewitz, Uwe und Hörmann, Stefan und Bosch, Esther Johanna (2021) The importance of considering individual differences in expression for in-vehicle emotion recognition - a machine learning approach. ACImobility, 2021-09-21 - 2021-09-22, Braunschweig.

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Kurzfassung

High frustration levels during driving are a critical problem for road safety in manual driving and play an essential role in the user experience in all automation levels. Therefore, the development of frustration-sensitive systems is an important step for building user focused systems that adapt its behavior to the user's needs. In this paper, we propose a fully automated process to detect frustration of users in video recordings from in-vehicle driver monitoring. This work uses a unique dataset containing frustrating driving situations that reflect real-world scenarios, which was collected in a driving simulator study with 50 participants with 6 drives each. Following each drive, the participants gave a post-hoc continuous frustration rating. Previous emotion research focused on finding universal expressions of emotion. However, scientific evidence suggests that each individual has a unique set of individual-specific expressions of frustration. Therefore, this work identifies frustration patters on an individual basis. Facial muscle movements have shown to be a promising indicator for emotion recognition. 19 facial muscle movements were detected with the commercially available software FACET and later used to train a Support Vector Machine and Gradient Boosted Tree. The method shows promising results in automated in-vehicle frustration recognition, which is a primary step towards the development of user experience sensitive driving assistance.

elib-URL des Eintrags:https://elib.dlr.de/144412/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:The importance of considering individual differences in expression for in-vehicle emotion recognition - a machine learning approach
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Le Houcq Corbi, Raquelraquel.lehoucq (at) gmail.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Ihme, KlasKlas.Ihme (at) dlr.dehttps://orcid.org/0000-0002-7911-3512NICHT SPEZIFIZIERT
Drewitz, UweUwe.Drewitz (at) dlr.dehttps://orcid.org/0000-0002-6542-9698NICHT SPEZIFIZIERT
Hörmann, Stefans.hoermann (at) tum.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Bosch, Esther JohannaEsther.Bosch (at) dlr.dehttps://orcid.org/0000-0002-6525-2650NICHT SPEZIFIZIERT
Datum:September 2021
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Name der Reihe:Proceedings of the ACIMobility Summit 2021
Status:veröffentlicht
Stichwörter:User Focused Systems; User Experience; Facial Expression Recognition; Emotion Recognition; Machine Learning
Veranstaltungstitel:ACImobility
Veranstaltungsort:Braunschweig
Veranstaltungsart:nationale Konferenz
Veranstaltungsbeginn:21 September 2021
Veranstaltungsende:22 September 2021
Veranstalter :ACImobility
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - NGC KoFiF (alt)
Standort: Braunschweig
Institute & Einrichtungen:Institut für Verkehrssystemtechnik > Informationsflussmodellierung in Mobilitätssystemen, BS
Hinterlegt von: Bosch, Esther Johanna
Hinterlegt am:12 Okt 2021 10:25
Letzte Änderung:24 Apr 2024 20:43

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