The DOI matches its Crossref record; 68 of the 76 references it cites were found in a registry or an index.
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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Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
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BackgroundMethodInfluentialA 3D Facial Expression Database For Facial Behavior Research
Yin, L., Wei, X., Sun, Y., Wang, J., & Rosato, M. J. (2006) · 7th International Conference on Automatic Face and Gesture Recognition (FGR06) · doi:10.1109/fgr.2006.6 · Cited by 1,171
Bosphorus Database for 3D Face Analysis
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., & Akarun, L. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-89991-4_6 · Cited by 812
Facial Expression Recognition via a Boosted Deep Belief Network
Liu, P., Han, S., Meng, Z., & Tong, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.233 · Cited by 667
Research works printed but not linked: 1
- li, 0, 3D facial expression recognition via multiple kernel learning of multi-scale local normal patterns, Proc 21st Int Conf Pattern Recog, 2577
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Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 2,175
A systematic review on affective computing: emotion models, databases, and recent advances
Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.03.009 · Cited by 713
A Survey on Human Face Expression Recognition Techniques
Revina, I. M., & Emmanuel, W. R. S. (2018) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2018.09.002 · Cited by 327
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-Expressions
Xia, Z., Hong, X., Gao, X., Feng, X., & Zhao, G. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2931351 · Cited by 296
Deep learning for human affect recognition: Insights and new developments
Rouast, P. V., Adam, M. T. P., & Chiong, R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2890471 · Cited by 243
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
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