The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
Identity-Aware Convolutional Neural Network for Facial Expression Recognition
Abstract
Facial expression recognition suffers under realworldconditions, especially on unseen subjects due to highinter-subject variations. To alleviate variations introduced bypersonal attributes and achieve better facial expression recognitionperformance, a novel identity-aware convolutional neuralnetwork (IACNN) is proposed. In particular, a CNN with a newarchitecture is employed as individual streams of a bi-streamidentity-aware network. An expression-sensitive contrastive lossis developed to measure the expression similarity to ensure thefeatures learned by the network are invariant to expressionvariations. More importantly, an identity-sensitive contrastiveloss is proposed to learn identity-related information from identitylabels to achieve identity-invariant expression recognition.Extensive experiments on three public databases including aspontaneous facial expression database have shown that theproposed IACNN achieves promising results in real world.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
(2008) · Journal of Machine Learning Research · Cited by 33,051
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
Learning a Similarity Metric Discriminatively, with Application to Face Verification
Chopra, S., Hadsell, R., & LeCun, Y. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.202 · Cited by 4,036
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
Zhao, G., & Pietikainen, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1110 · Cited by 2,846
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.52 · Cited by 2,797
Cited by
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
Wang, K., Peng, X., Yang, J., Meng, D., & Qiao, Y. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2956143 · Cited by 940
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
Fard, A. P., & Mahoor, M. H. (2022) · IEEE Access · doi:10.1109/access.2022.3156598 · Cited by 157
FERAtt: Facial Expression Recognition With Attention Net
Fernandez, P. D. M., Pena, F. A. G., Ren, T. I., & Cunha, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00112 · Cited by 142
Metadata
- Authors
- Meng, Z., Liu, P., Cai, J., Han, S., & Tong, Y.
- Venue
- 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 558-565
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W2730601341
- Cited by
- 319
- References
- 55
- FWCI
- 36.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Meng, Z., Liu, P., Cai, J., Han, S., & Tong, Y. (2017). Identity-Aware Convolutional Neural Network for Facial Expression Recognition. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). https://doi.org/10.1109/fg.2017.140
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