Emotion Recognition Techniques with IoT and Deep Learning Technologies-Reference-Cited by-同舟云学术

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Emotion Recognition Techniques with IoT and Deep Learning Technologies

Published:2023 Issue: Volume: Page:143-152
ISSN:2367-3370
Container-title:Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23)
language:
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Author:

Méndez-Porras Abel,Campaña-Bastidas Sixto Enrique,Diaz-Toro Andres Alejandro,Calvo-Valverde Luis-Alexander,Rosero Rolando Rigoberto

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-33743-7_12

Reference32 articles.

1. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807 (2017)

2. de Toledo, G.L., Marcacini, R.M.: Transfer learning with joint fine-tuning for multimodal sentiment analysis (2022)

3. Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild (2019)

4. Efremova, N., Patkin, M., Sokolov, D.: Face and emotion recognition with neural networks on mobile devices: practical implementation on different platforms. In: 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), pp. 1–5. IEEE (2019)

5. FER-2013 Learn facial expressions from an image (2023). https://www.kaggle.com/datasets/msambare/fer2013/

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1. In the Blink of an Eye: Insomnia Microexpression Classification Using CNN-LSTM;2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM);2024-02-21

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