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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.13 No.1
- 발행연도
- 2018.1
- 수록면
- 485 - 492 (8page)
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초록· 키워드
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In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as ‘expressionless’, ‘happy’, ‘sad’, ‘angry’, ‘surprised’ and ‘disgusted’. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.
목차
Abstract
1. Introduction
2. Database Configuration
3. Proposed CNN Architecture
4. Experiments and results
5. Conclusion
References
참고문헌 (31)
참고문헌 신청
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
Y. Taigman, M. Yang, M. Ranzato and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014.
H. Jung, S. Lee, J. Yim, S. Park and J. Kim, “Joint fine-tuning in deep neural networks for facial expression recognition,” Proceedings of the IEEE International Conference on Computer Vision, 2015.
AT Lopes, E de Aguiar and AF De Souza, “Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order,” Pattern Recognition, vol. 61, pp.610- 628, 2017.
P. Burkert, F. Trier, M.Z. Afzal, A. Dengel and M. Liwichki, “Dexpression: Deep convolutional neural network for expression recognition,” arXiv preprint arXiv:1509.05371, 2015.
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UCI(KEPA) : I410-ECN-0101-2018-560-001704970