Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types

Ian Mills; Frances Cleary

doi:10.1109/vrw55335.2022.00282

Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types

Ian Mills, Frances Cleary

Research output: Contribution to conference › Poster › peer-review

1 Citation (Scopus)

Abstract

This research poster outlines the initial development of a facial emotion recognition (FER) evaluation system based on RGB-D imagery captured via a mobile based device. The study outlined features control group of non-occluded facial types and a set of participants wearing a head mounted display (HMD) in order to demonstrate an occluded facial type. We explore an architecture to develop a FER system that is suitable for occluded facial analysis. This paper details the methodology, experimental design and future work to be carried out to deliver such a system.

Original language	English
Pages	862-863
Number of pages	2
DOIs	https://doi.org/10.1109/vrw55335.2022.00282
Publication status	Published - 01 Mar 2022
Event	2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022 - Virtual, Online, New Zealand Duration: 12 Mar 2022 → 16 Mar 2022

Conference

Conference	2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
Country/Territory	New Zealand
City	Virtual, Online
Period	12/03/2022 → 16/03/2022

Keywords

Accessibility
Accessibility Technologies
Collaborative and Social Computing
Human computer interaction
Human Computer Interaction (HCI)
Human-centered computing
Interaction Design
Interaction paradigms
Virtual reality
Visualization

Access to Document

10.1109/vrw55335.2022.00282

Cite this

Mills, I., & Cleary, F. (2022). Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types. 862-863. Poster session presented at 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022, Virtual, Online, New Zealand. https://doi.org/10.1109/vrw55335.2022.00282

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title = "Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types",

abstract = "This research poster outlines the initial development of a facial emotion recognition (FER) evaluation system based on RGB-D imagery captured via a mobile based device. The study outlined features control group of non-occluded facial types and a set of participants wearing a head mounted display (HMD) in order to demonstrate an occluded facial type. We explore an architecture to develop a FER system that is suitable for occluded facial analysis. This paper details the methodology, experimental design and future work to be carried out to deliver such a system.",

keywords = "Accessibility, Accessibility Technologies, Collaborative and Social Computing, Human computer interaction, Human Computer Interaction (HCI), Human-centered computing, Interaction Design, Interaction paradigms, Virtual reality, Visualization",

author = "Ian Mills and Frances Cleary",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022 ; Conference date: 12-03-2022 Through 16-03-2022",

year = "2022",

month = mar,

day = "1",

doi = "10.1109/vrw55335.2022.00282",

language = "English",

pages = "862--863",

}

Mills, I & Cleary, F 2022, 'Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types', 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022, Virtual, Online, New Zealand, 12/03/2022 - 16/03/2022 pp. 862-863. https://doi.org/10.1109/vrw55335.2022.00282

Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types. / Mills, Ian ; Cleary, Frances.
2022. 862-863 Poster session presented at 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022, Virtual, Online, New Zealand.

Research output: Contribution to conference › Poster › peer-review

TY - CONF

T1 - Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types

AU - Mills, Ian

AU - Cleary, Frances

PY - 2022/3/1

Y1 - 2022/3/1

N2 - This research poster outlines the initial development of a facial emotion recognition (FER) evaluation system based on RGB-D imagery captured via a mobile based device. The study outlined features control group of non-occluded facial types and a set of participants wearing a head mounted display (HMD) in order to demonstrate an occluded facial type. We explore an architecture to develop a FER system that is suitable for occluded facial analysis. This paper details the methodology, experimental design and future work to be carried out to deliver such a system.

AB - This research poster outlines the initial development of a facial emotion recognition (FER) evaluation system based on RGB-D imagery captured via a mobile based device. The study outlined features control group of non-occluded facial types and a set of participants wearing a head mounted display (HMD) in order to demonstrate an occluded facial type. We explore an architecture to develop a FER system that is suitable for occluded facial analysis. This paper details the methodology, experimental design and future work to be carried out to deliver such a system.

KW - Accessibility

KW - Accessibility Technologies

KW - Collaborative and Social Computing

KW - Human computer interaction

KW - Human Computer Interaction (HCI)

KW - Human-centered computing

KW - Interaction Design

KW - Interaction paradigms

KW - Virtual reality

KW - Visualization

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U2 - 10.1109/vrw55335.2022.00282

DO - 10.1109/vrw55335.2022.00282

M3 - Poster

SP - 862

EP - 863

T2 - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022

Y2 - 12 March 2022 through 16 March 2022

ER -

Facial Emotion recognition analysis using deep learning through RGB-D imagery of VR participants through partially occluded facial types

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Keywords

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