Local normal binary patterns for 3D facial action unit detection
Abstract
This paper proposes a new feature descriptor, local normal binary patterns (LNBPs), which is exploited for detection of facial action units (AUs). After LNBPs have been employed to form descriptor vectors, which capture the detailed shape of the action, feature selection is performed via a Gentle-Boost (GB) algorithm, and support vector machines (SVMs) are trained to detect each AU. This process was tested on the Bosphorus database, alongside the same test using 3D local binary pattern (3DLBP) descriptors which apply the LBP operator to the depth map of the face. LNBP descriptors were demonstrated to outperform 3DLBPs in detection of many individual AUs. Finally, feature fusion was used to combine the benefits of the 3DLBPs and each of the LNBP descriptors, with the best result achieving a mean ROC AuC of 96.35.
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Most 3D methods target primary expressions but studies about AUs were published as well.
“Most of them target primary expressions [131] but studies about AUs were published as well [122], [218].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [122]
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References 13 · Cited by 56
- Authors
- Sandbach, G., Zafeiriou, S., & Pantic, M.
- Affiliations
- Imperial College London (GB)
- ORCID
- Zafeiriou, Stefanos
- Venue
- 2012 19th IEEE International Conference on Image Processing
- Publisher
- IEEE
- Published
- 2012-09
- Pages
- 1813-1816
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2063568356
- Cited by
- 56
- References
- 13
- Citations by year
- 2026: 1 · 2023: 3 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 3 · 2017: 7 · 2016: 6 · 2015: 12 · 2014: 8 · 2013: 2 · 2012: 1
- FWCI
- 5.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- University of Twente Research Information (publishedVersion)
- University of Twente Research Information (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/H016988/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- facial action units, facial expression analysis, feature descriptors, feature selection, support vector machine, feature fusion, depth images
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