Deep Entwined Learning Head Pose and Face Alignment Inside an Attentional Cascade with Doubly-Conditional fusion
Open accessAbstract
Head pose estimation and face alignment constitute a backbone preprocessing for many applications relying on face analysis. While both are closely related tasks, they are generally addressed separately, e.g. by deducing the head pose from the landmark locations. In this paper, we propose to entwine face alignment and head pose tasks inside an attentional cascade. This cascade uses a geometry transfer network for integrating heterogeneous annotations to enhance landmark localization accuracy. Furthermore, we propose a doubly-conditional fusion scheme to select relevant feature maps, and regions thereof, based on a current head pose and landmark localization estimate. We empirically show the benefit of entwining head pose and landmark localization objectives inside our architecture, and that the proposed AC-DC model enhances the state-of-the-art accuracy on multiple databases for both face alignment and head pose estimation tasks.
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The alignment task can be divided into attentional alignment and semantic alignment.
“Generally, the alignment task can be divided into two subtasks: the attentional alignment task [170, 171] and the semantic alignment task [172, 173].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [170]
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Cited by
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- Authors
- Dapogny, A., Bailly, K., & Cord, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Venue
- 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
- Publisher
- IEEE
- Published
- 2020-11
- Pages
- 192-198
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W3016976725
- Cited by
- 8
- References
- 34
- Citations by year
- 2024: 3 · 2023: 3 · 2021: 2
- FWCI
- 0.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- International Conference on Automatic Face and Gesture Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Funders
- Agence Nationale de la Recherche (ANR-17-CE33-0002)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- head pose estimation, facial landmark detection, multi-task learning, facial analysis
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