The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Learning active facial patches for expression analysis
Abstract
In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., around mouth, eye), we try to discover the common and specific patches which are important to discriminate all the expressions and only a particular expression, respectively. A two-stage multi-task sparse learning (MTSL) framework is proposed to efficiently locate those discriminative patches. In the first stage MTSL, expression recognition tasks, each of which aims to find dominant patches for each expression, are combined to located common patches. Second, two related tasks, facial expression recognition and face verification tasks, are coupled to learn specific facial patches for individual expression. Extensive experiments validate the existence and significance of common and specific patches. Utilizing these learned patches, we achieve superior performances on expression recognition compared to the state-of-the-arts.
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Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
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Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Social signal processing: Survey of an emerging domain
Vinciarelli, A., Pantic, M., & Bourlard, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.11.007 · Cited by 842
Cited by
Deep Facial Expression Recognition: A Survey
Li, S., & Deng, W. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981446 · Cited by 1,753
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
Lopes, A. T., de Aguiar, E., De Souza, A. F., & Oliveira-Santos, T. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.026 · Cited by 799
Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition
Jung, H., Lee, S., Yim, J., Park, S., & Kim, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.341 · Cited by 781
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition
Sariyanidi, E., Gunes, H., & Cavallaro, A. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2366127 · Cited by 685
Metadata
- Authors
- Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., & Metaxas, D. N.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2562-2569
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2088575594
- Cited by
- 369
- References
- 4
- FWCI
- 16.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., & Metaxas, D. N. (2012). Learning active facial patches for expression analysis. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247974
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