The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Abstract
We present a novel method for classifying emotions from static facial images. Our approach leverages on the recent success of Convolutional Neural Networks (CNN) on face recognition problems. Unlike the settings often assumed there, far less labeled data is typically available for training emotion classification systems. Our method is therefore designed with the goal of simplifying the problem domain by removing confounding factors from the input images, with an emphasis on image illumination variations. This, in an effort to reduce the amount of data required to effectively train deep CNN models. To this end, we propose novel transformations of image intensities to 3D spaces, designed to be invariant to monotonic photometric transformations. These are applied to CASIA Webface images which are then used to train an ensemble of multiple architecture CNNs on multiple representations. Each model is then fine-tuned with limited emotion labeled training data to obtain final classification models. Our method was tested on the Emotion Recognition in the Wild Challenge (EmotiW 2015), Static Facial Expression Recognition sub-challenge (SFEW) and shown to provide a substantial, 15.36% improvement over baseline results (40% gain in performance).
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“Studies running experiments on this technique report accuracy improvements (e.g., from 79% to 89% in [87]; see also [74], [94]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [74]
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Flagged first, then by citations
Research works printed but not linked: 5
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Cited by
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Deep Facial Expression Recognition: A Survey
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Li, S., & Deng, W. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2868382 · Cited by 688
Metadata
- Authors
- Levi, G., & Hassner, T.
- Affiliations
- Open University of Israel (IL)
- University of Southern California (US)
- ORCID
- Hassner, Tal
- Venue
- Proceedings of the 2015 ACM on International Conference on Multimodal Interaction
- Publisher
- ACM
- Published
- 2015-11-09
- Pages
- 503-510
- OpenAlex
- W2251198138
- Cited by
- 336
- References
- 46
- Citations by year
- 2026: 4 · 2025: 15 · 2024: 17 · 2023: 17 · 2022: 24 · 2021: 48 · 2020: 49 · 2019: 62 · 2018: 46 · 2017: 44 · 2016: 9 · 2015: 1
- FWCI
- 44.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Funders
- IARPA (2014-14071600010)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- 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
- Keywords
- emotion recognition, facial expression recognition, static facial images, convolutional neural networks, illumination invariance, photometric transformations, CASIA-WebFace
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