The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Efficient Facial Expression Recognition Algorithm Based on Hierarchical Deep Neural Network Structure
Open accessAbstract
With the continued development of artificial intelligence (AI) technology, research on interaction technology has become more popular. Facial expression recognition (FER) is an important type of visual information that can be used to understand a human's emotional situation. In particular, the importance of AI systems has recently increased due to advancements in research on AI systems applied to AI robots. In this paper, we propose a new scheme for FER system based on hierarchical deep learning. The feature extracted from the appearance feature-based network is fused with the geometric feature in a hierarchical structure. The appearance feature-based network extracts holistic features of the face using the preprocessed LBP image, whereas the geometric feature-based network learns the coordinate change of action units (AUs) landmark, which is a muscle that moves mainly when making facial expressions. The proposed method combines the result of the softmax function of two features by considering the error associated with the second highest emotion (Top-2) prediction result. In addition, we propose a technique to generate facial images with neutral emotion using the autoencoder technique. By this technique, we can extract the dynamic facial features between the neutral and emotional images without sequence data. We compare the proposed algorithm with the other recent algorithms for CK+ and JAFFE dataset, which are typically considered to be verified datasets in the facial expression recognition. The ten-fold cross validation results show 96.46% of accuracy in the CK+ dataset and 91.27% of accuracy in the JAFFE dataset. When comparing with other methods, the result of the proposed hierarchical deep network structure shows up to about 3% of the accuracy improvement and 1.3% of average improvement in CK+ dataset, respectively. In JAFFE datasets, up to about 7% of the accuracy is enhanced, and the average improvement is verified by about 1.5%.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2014) · Cited by 33,419
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“We used a preprocessing technique involving bilateral blurring [25].”
MethodCootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
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
One millisecond face alignment with an ensemble of regression trees
Kazemi, V., & Sullivan, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.241 · Cited by 2,831
Dlib-ml: A Machine Learning Toolkit
King, D. E. (2009) · doi:10.5555/1577069.1755843 · Cited by 2,777
Cited by
Recognition of facial expression of fetuses by artificial intelligence (AI)
Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A., & Miyake, T. (2021) · Journal of Perinatal Medicine · doi:10.1515/jpm-2020-0537 · Cited by 34
Miyagi, Y., Hata, T., Bouno, S., Koyanagi, A., & Miyake, T. (2023) · International Journal of Gynecology & Obstetrics · doi:10.1002/ijgo.14569 · Cited by 15
Metadata
- Authors
- Kim, J.-H., Kim, B.-G., Roy, P. P., & Jeong, D.-M.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 41273-41285
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2931011950
- Cited by
- 303
- References
- 34
- FWCI
- 48.68
- Type
- journal article
- Language
- en
- Open access
- gold
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