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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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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
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.1
- 발행연도
- 2024.2
- 수록면
- 61 - 68 (8page)
- DOI
- 10.5573/IEIESPC.2024.13.1.61
이용수
초록· 키워드
오류제보하기
In the context of artificial intelligence technology, an emotion recognition (ER) has numerous roles in human lives. On the other hand, the emotion recognition techniques most currently used perform poorly in recognizing emotions, which limits their wide spread use in practical applications. A Collaborative Multimodal Emotion Recognition through Improved Federated Learning Generative Adversarial Network (MER-IFLGAN) for facial expressions and electro encephalogram (EEG) signals was proposed to reduce this issue. Multi-resolution binarized image feature extraction (MBIFE) was initially used for facial expression feature extraction. The EEG features were extracted using the Dwarf Mongoose Optimization (DMO) algorithm. Finally, IFLGAN completes the Emotion recognition task. The proposed technique was simulated in MATLAB. The proposed technique achieved 25.45% and 19.71% higher accuracy and a 32.01% and 39.11% shorter average processing time compared to the existing models, like EEG based Cross-subject and Cross-modal Model (CSCM) for Multimodal Emotion Recognition (MERCSCM) and Long-Short Term Memory Model (LSTM) for EEG Emotion Recognition (MERLSTM), respectively. The experimental results of the proposed model shows that complementing EEG signals with the features of facial expression could identify four types of emotions: happy, sad, fear, and neutral. Further more, the IFLGAN classifier can enhance the capacity of multimodal emotion recognition.
목차
Abstract
1. Introduction
2. Literature Survey
3. Proposed Methodology
4. Result and Discussion
5. Conclusion
References
참고문헌 (28)
참고문헌 신청
E.S. Salama, R.A. El-Khoribi, M.E. Shoman, M.A.W. Shalaby, “A 3D-convolutional neural network framework with ensemble learning techniques for multi-modal emotion recognition.” Egyptian Informatics Journal, vol. 22, no. 2, pp. 167-176. 2021. Article (CrossRef Link)
Y. Wu, J. Li, “Multi-modal emotion identification fusing facial expression and EEG.” Multimedia Tools and Applications, vol. 82, no. 7, pp. 10901-10919. 2023. Article (CrossRef Link)
L. Fang, S.P. Xing, Z. Ma, Z. Zhang, Y. Long, K.P. Lee, S.J. Wang, “Emo-MG Framework: LSTM-based Multi-modal Emotion Detection through Electro encephalography Signals and Micro Gestures.” International Journal of Human–Computer Interaction, vol. 1, no. 1, pp. 1-17. 2023. Article (CrossRef Link)
F. H. Shajin, B. Aruna Devi, N. B. Prakash, G. R. Sreekanth, P. Rajesh, “Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation.” Soft Computing, pp. 1-26. Apr. 2023. Article (CrossRef Link)
F. H. Shajin, P. Rajesh, M. R. Raja, “An efficient VLSI architecture for fast motion estimation exploiting zero motion pre judgment technique and a new quadrant-based search algorithm in HEVC.” Circuits, Systems, and Signal Processing, pp. 1-24. Mar. 2022. Article (CrossRef Link)
Y. Wu, J. Li, “Multi-modal emotion identification fusing facial expression and EEG.” Multimedia Tools and Applications, vol. 82, no. 7, pp. 10901-10919. 2023. Article (CrossRef Link)
L. Fang, S.P. Xing, Z. Ma, Z. Zhang, Y. Long, K.P. Lee, S.J. Wang, “Emo-MG Framework: LSTM-based Multi-modal Emotion Detection through Electro encephalography Signals and Micro Gestures.” International Journal of Human–Computer Interaction, vol. 1, no. 1, pp. 1-17. 2023. Article (CrossRef Link)
F. H. Shajin, B. Aruna Devi, N. B. Prakash, G. R. Sreekanth, P. Rajesh, “Sailfish optimizer with Levy flight, chaotic and opposition-based multi-level thresholding for medical image segmentation.” Soft Computing, pp. 1-26. Apr. 2023. Article (CrossRef Link)
F. H. Shajin, P. Rajesh, M. R. Raja, “An efficient VLSI architecture for fast motion estimation exploiting zero motion pre judgment technique and a new quadrant-based search algorithm in HEVC.” Circuits, Systems, and Signal Processing, pp. 1-24. Mar. 2022. Article (CrossRef Link)
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