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Multimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network
Abstract
This paper presents a novel and efficient deep fusion convolutional neural network (DF-CNN) for multimodal 2D+3D facial expression recognition (FER). DF-CNN comprises a feature extraction subnet, a feature fusion subnet, and a softmax layer. In particular, each textured three-dimensional (3D) face scan is represented as six types of 2D facial attribute maps (i.e., geometry map, three normal maps, curvature map, and texture map), all of which are jointly fed into DF-CNN for feature learning and fusion learning, resulting in a highly concentrated facial representation (32-dimensional). Expression prediction is performed by two ways: 1) learning linear support vector machine classifiers using the 32-dimensional fused deep features, or 2) directly performing softmax prediction using the six-dimensional expression probability vectors. Different from existing 3D FER methods, DF-CNN combines feature learning and fusion learning into a single end-to-end training framework. To demonstrate the effectiveness of DF-CNN, we conducted comprehensive experiments to compare the performance of DFCNN with handcrafted features, pre-trained deep features, finetuned deep features, and state-of-the-art methods on three 3D face datasets (i.e., BU-3DFE Subset I, BU-3DFE Subset II, and Bosphorus Subset). In all cases, DF-CNN consistently achieved the best results. To the best of our knowledge, this is the first work of introducing deep CNN to 3D FER and deep learning-based featurelevel fusion for multimodal 2D+3D FER.
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“Improvements in accuracy are reported when following this approach (e.g., from 39% to 42% in [89]; see also [99], [92]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [92]
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A Motion Flow Guided MicroNet Framework for Micro Expression RecognitionPreprint
Verma, M., Vipparthi, S. K., & Singh, G. (2022) · doi:10.36227/techrxiv.20085557
Willingness of Sharing Facial Data for Emotion Recognition - A Case Study in the Insurance MarketPreprint
Ferrari, A., Mangano, G., Rafele, C., Vezzetti, E., & Marcolin, F. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4159601
Metadata
- Authors
- Li, H., Sun, J., Xu, Z., & Chen, L.
- Affiliations
- Xi'an Jiaotong University (CN)
- École Centrale de Lyon (FR)
- Venue
- IEEE Transactions on Multimedia
- Published
- 2017-12
- Volume
- 19
- Issue
- 12
- Pages
- 2816-2831
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2621864722
- Cited by
- 211
- References
- 76
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 13 · 2023: 16 · 2022: 27 · 2021: 46 · 2020: 35 · 2019: 49 · 2018: 11
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Multimedia (publishedVersion)
- Funders
- NSFC (11401464, 61472313, 11622106)
- Chinese Postdoctoral Science Foundation (2014M560785)
- International Exchange Foundation of China NSFC
- United Kingdom RS (61711530242)
- French Research Agency
- l’Agence Nationale de Recherche (ANR-13-CORD-0004-02, ANR-13-INSE-0004-02)
- Partner University Foundation
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression recognition, 3D facial scans, multimodal fusion, feature fusion, normal maps, curvature maps, end-to-end learning, BU-3DFE
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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