The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.
Facial Expression Analysis under Partial Occlusion: A Survey
Open accessAbstract
Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.
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Cites
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A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
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Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
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Automatic facial expression analysis: a survey
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“In earlier surveys on FEA [Pantic and Rothkrantz 2000], [Fasel and Luettin 2003], no study reported specifically designed techniques to overcome facial occlusion.”
BackgroundMethodInfluentialEmotion in the Human Face: Guidelines for Research and an Integration of Findings
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A 3D Facial Expression Database For Facial Behavior Research
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Cited by
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
Li, Y., Zeng, J., Shan, S., & Chen, X. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2886767 · Cited by 916
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Recommender Systems: Techniques, Applications, and Challenges
Ricci, F., Rokach, L., & Shapira, B. (2022) · Recommender Systems Handbook · doi:10.1007/978-1-0716-2197-4_1 · Cited by 214
Bias in Large Language Models: Origin, Evaluation, and Mitigation
Guo, Y., Guo, M., Su, J., Yang, Z., Zhu, M., Li, H., Qiu, M., & Liu, S. S. (2024) · arXiv · doi:10.48550/arxiv.2411.10915 · Cited by 208
Facial Expression Recognition: A Survey
Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195
Metadata
- Authors
- Zhang, L., Verma, B., Tjondronegoro, D., & Chandran, V.
- Venue
- ACM Computing Surveys
- Published
- 2018-04-18
- Volume
- 51
- Issue
- 2
- Pages
- 1-49
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2788728386
- Cited by
- 1,258
- References
- 114
- FWCI
- 89.77
- Type
- journal article
- Language
- en
- Open access
- green
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