The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Going deeper in facial expression recognition using deep neural networks
Open accessAbstract
Automated Facial Expression Recognition (FER) has remained a challenging and interesting problem in computer vision. Despite efforts made in developing various methods for FER, existing approaches lack generalizability when applied to unseen images or those that are captured in wild setting (i.e. the results are not significant). Most of the existing approaches are based on engineered features (e.g. HOG, LBPH, and Gabor) where the classifier's hyper-parameters are tuned to give best recognition accuracies across a single database, or a small collection of similar databases. This paper proposes a deep neural network architecture to address the FER problem across multiple well-known standard face datasets. Specifically, our network consists of two convolutional layers each followed by max pooling and then four Inception layers. The network is a single component architecture that takes registered facial images as the input and classifies them into either of the six basic or the neutral expressions. We conducted comprehensive experiments on seven publicly available facial expression databases, viz. MultiPIE, MMI, CK+, DISFA, FERA, SFEW, and FER2013. The results of our proposed architecture are comparable to or better than the state-of-the-art methods and better than traditional convolutional neural networks in both accuracy and training time.
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Cites
ImageNet classification with deep convolutional neural networks
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ImageNet Large Scale Visual Recognition Challenge
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Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
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Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark
Dhall, A., Goecke, R., Lucey, S., & Gedeon, T. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130508 · Cited by 541 · cited 10×
“We evaluate the proposed method on well-known publicly available facial expression databases: CMU MultiPIE [15], MMI [36], Denver Intensity of Spontaneous Facial Actions (DISFA) [29], extended CK+ [27], GEMEPFERA database [5], SFEW [10], and FER2013 [1].”
MethodInfluentialThe first facial expression recognition and analysis challenge
Valstar, M. F., Jiang, B., Mehu, M., Pantic, M., & Scherer, K. (2011) · Face and Gesture 2011 · doi:10.1109/fg.2011.5771374 · Cited by 385
Cited by
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Self-citationDeep 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
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Li, S., Deng, W., & Du, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.277 · Cited by 1,470
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
Spatial–Temporal Recurrent Neural Network for Emotion Recognition
Zhang, T., Zheng, W., Cui, Z., Zong, Y., & Li, Y. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2788081 · Cited by 595
Metadata
- Authors
- Mollahosseini, A., Chan, D., & Mahoor, M. H.
- Venue
- 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 1-10
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2244142460
- Cited by
- 1,086
- References
- 10
- FWCI
- 114.96
- Type
- conference paper
- Language
- en
- Open access
- green
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