Facial expression recognition in the wild based on multimodal texture features
Open accessAbstract
Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal–spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.
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Researchers report improvements in accuracy by applying intensity normalization (e.g., from 54% to 57% in [94]).
“Researchers report improvements in accuracy by applying intensity normalization (e.g., from 54% to 57% in [94]; see also [98]).”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [98]
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Ganatra, N., Patel, S., Patel, R., Khant, S., & Patel, A. M. (2022) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict53079.2022.9768508 · Cited by 1 · see citing works
Novel multi‐scale deep residual attention network for facial expression recognition
Liu, D., Wang, L., Wang, Z., & Chen, L. (2020) · The Journal of Engineering · doi:10.1049/joe.2020.0183 · Cited by 1 · see citing works
Happy Emotion Recognition in Videos Via Apex Spotting and Temporal Models
Samadiani, N., & Huang, G. (2020) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat50758.2020.00077
Development of a Spontaneous Academic Emotion Database of Middle School Students in Online LearningPreprint
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Expression Recognition Using a Flow-Based Latent-Space Representation
Aathreya, S., & Canavan, S. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37745-7_11
Investigating Dense Cnn Architectures: A Case Study of Facial Emotional Recognition SystemsPreprint
Kyei, E. A., Appiahene, P., Ayidzoe, M. A., Appiah, O., Asare, J. W., Freeman, E., Brown‐Acquaye, W. L., & Koi-Akrofi, G. Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4853155
Information Technologies for Early Detection of Alzheimer’s Disease in the Elderly: A Survey
Liu, Z. Q., Fei, Z., Zhou, W., & Fei, M. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0188-2_24
To Identify the Face Recognition for Images and Videos using Deep Learning Approaches
Shukla, R. K., Kumar, R., & Buhari, A. (2025) · International Conference Communication, Networks & Computing · doi:10.1109/cnc68716.2025.11484803
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- Authors
- Sun, B., Li, L., Zhou, G., & He, J.
- Affiliations
- Beijing Normal University (CN)
- Venue
- Journal of Electronic Imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2016-06-22
- Volume
- 25
- Issue
- 06
- Pages
- 1-8
- ISSN
- 1017-9909 (ISSN-L)
- 1560-229X
- OpenAlex
- W2474193198
- Cited by
- 74
- References
- 41
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 11 · 2020: 14 · 2019: 12 · 2018: 3 · 2017: 10 · 2016: 2
- FWCI
- 12.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Electronic Imaging (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (61501035)
- Natural Science Foundation of China (61501035, KJZXCJ2016042)
- Fundamental Research Funds for the Central Universities (2014KJJCA15)
- Central Universities of China (2014KJJCA15)
- Fundamental Research Funds for the Central Universities of China (2014KJJCA15)
- Ministry of Education (DCA140229)
- National Education Science Twelfth Five-Year Plan Key Issues of the Ministry of Education (DCA140229)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › 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
- facial expression recognition, in-the-wild, multimodal fusion, texture features, dense SIFT, spatio-temporal features, deep convolutional neural network features, decision fusion
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