Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911793511661568 |
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| author | Witherow, Megan A. Butler, Crystal Shields, Winston J. Ilgin, Furkan Diawara, Norou Keener, Janice Harrington, John W. Iftekharuddin, Khan M. |
| author_facet | Witherow, Megan A. Butler, Crystal Shields, Winston J. Ilgin, Furkan Diawara, Norou Keener, Janice Harrington, John W. Iftekharuddin, Khan M. |
| contents | Customizable 3D avatar-based facial expression stimuli may improve user engagement in behavioral biomarker discovery and therapeutic intervention for autism, Alzheimer's disease, facial palsy, and more. However, there is a lack of customizable avatar-based stimuli with Facial Action Coding System (FACS) action unit (AU) labels. Therefore, this study focuses on (1) FACS-labeled, customizable avatar-based expression stimuli for maintaining subjects' engagement, (2) learning-based measurements that quantify subjects' facial responses to such stimuli, and (3) validation of constructs represented by stimulus-measurement pairs. We propose Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) labeled with AUs by a certified FACS expert. To measure subjects' AUs in response to CADyFACE, we propose a novel Beta-guided Correlation and Multi-task Expression learning neural network (BeCoME-Net) for multi-label AU detection. The beta-guided correlation loss encourages feature correlation with AUs while discouraging correlation with subject identities for improved generalization. We train BeCoME-Net for unilateral and bilateral AU detection and compare with state-of-the-art approaches. To assess construct validity of CADyFACE and BeCoME-Net, twenty healthy adult volunteers complete expression recognition and mimicry tasks in an online feasibility study while webcam-based eye-tracking and video are collected. We test validity of multiple constructs, including face preference during recognition and AUs during mimicry. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement Witherow, Megan A. Butler, Crystal Shields, Winston J. Ilgin, Furkan Diawara, Norou Keener, Janice Harrington, John W. Iftekharuddin, Khan M. Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning Customizable 3D avatar-based facial expression stimuli may improve user engagement in behavioral biomarker discovery and therapeutic intervention for autism, Alzheimer's disease, facial palsy, and more. However, there is a lack of customizable avatar-based stimuli with Facial Action Coding System (FACS) action unit (AU) labels. Therefore, this study focuses on (1) FACS-labeled, customizable avatar-based expression stimuli for maintaining subjects' engagement, (2) learning-based measurements that quantify subjects' facial responses to such stimuli, and (3) validation of constructs represented by stimulus-measurement pairs. We propose Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) labeled with AUs by a certified FACS expert. To measure subjects' AUs in response to CADyFACE, we propose a novel Beta-guided Correlation and Multi-task Expression learning neural network (BeCoME-Net) for multi-label AU detection. The beta-guided correlation loss encourages feature correlation with AUs while discouraging correlation with subject identities for improved generalization. We train BeCoME-Net for unilateral and bilateral AU detection and compare with state-of-the-art approaches. To assess construct validity of CADyFACE and BeCoME-Net, twenty healthy adult volunteers complete expression recognition and mimicry tasks in an online feasibility study while webcam-based eye-tracking and video are collected. We test validity of multiple constructs, including face preference during recognition and AUs during mimicry. |
| title | Customizable Avatars with Dynamic Facial Action Coded Expressions (CADyFACE) for Improved User Engagement |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.07314 |