The DOI matches its Crossref record; 179 of the 179 references it cites were found in a registry or an index.
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Open accessAbstract
Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions. In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them. In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions. We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition. We discuss the role of these components and highlight the models and new trends that are followed in their design. Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias. This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems.
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Cited by
Openface 2.0: facial behavior analysis toolkit
Baltrusaitis, T., Zadeh, A., Lim, Y. C., & Morency, L.-P. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00019 · Cited by 1,861
Deep Facial Expression Recognition: A Survey
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OpenFace: An open source facial behavior analysis toolkit
Baltrusaitis, T., Robinson, P., & Morency, L.-P. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477553 · Cited by 1,747
Facial Expression Analysis under Partial Occlusion: A Survey
Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V. (2019) · ACM Computing Surveys · doi:10.1145/3158369 · Cited by 1,258
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657
Metadata
- Authors
- Sariyanidi, E., Gunes, H., & Cavallaro, A.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2015-06-01
- Volume
- 37
- Issue
- 6
- Pages
- 1113-1133
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1965947362
- Cited by
- 685
- References
- 179
- FWCI
- 71.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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