A review of advancements in driver emotion detection: deep learning approaches and dataset analysis

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dc.contributor.authorAlfaras, Mohammed Shukur
dc.contributor.authorKaran, Oǧuz
dc.date.accessioned2024-07-18T11:35:09Z
dc.date.available2024-07-18T11:35:09Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThis review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in Machine Learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers' emotions.en_US
dc.identifier.citationAlfaras, M. S., Karan, O. (2024). A review of advancements in driver emotion detection: deep learning approaches and dataset analysis. 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024. 10.1109/IRASET60544.2024.10549432en_US
dc.identifier.isbn9798350309508
dc.identifier.scopus2-s2.0-85197118387
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4759
dc.indekslendigikaynakScopus
dc.institutionauthorAlfaras, Mohammed Shukur
dc.institutionauthorKaran, Oǧuz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024
dc.relation.isversionof10.1109/IRASET60544.2024.10549432en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep Learning Methodologiesen_US
dc.subjectDiverse Datasetsen_US
dc.subjectDriver Emotion Detectionen_US
dc.subjectFacial Emotion Recognitionen_US
dc.subjectRoad Safetyen_US
dc.titleA review of advancements in driver emotion detection: deep learning approaches and dataset analysis
dc.typeConference Object

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