Standard

Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network. / Hashan, Antor mahamudul; Al-Saeedi adnan adhab, K; Islam, Rizu md rakib ul et al.
Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 113-117.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Hashan, AM, Al-Saeedi adnan adhab, K, Islam, RMRU, Avinash, K & Dey, S 2023, Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network. in Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 113-117, 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 13/07/2023. https://doi.org/10.1109/IAICT59002.2023.10205785

APA

Hashan, A. M., Al-Saeedi adnan adhab, K., Islam, R. M. R. U., Avinash, K., & Dey, S. (2023). Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network. In Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023: book (pp. 113-117). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAICT59002.2023.10205785

Vancouver

Hashan AM, Al-Saeedi adnan adhab K, Islam RMRU, Avinash K, Dey S. Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network. In Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 113-117 doi: 10.1109/IAICT59002.2023.10205785

Author

Hashan, Antor mahamudul ; Al-Saeedi adnan adhab, K ; Islam, Rizu md rakib ul et al. / Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network. Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 113-117

BibTeX

@inproceedings{1e30841542684221ac6cf1d4dfee4512,
title = "Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network",
abstract = "The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.",
author = "Hashan, {Antor mahamudul} and {Al-Saeedi adnan adhab}, K and Islam, {Rizu md rakib ul} and Kumar Avinash and Subhankar Dey",
year = "2023",
month = jul,
day = "13",
doi = "10.1109/IAICT59002.2023.10205785",
language = "English",
isbn = "979-835031363-5",
pages = "113--117",
booktitle = "Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) ; Conference date: 13-07-2023 Through 15-07-2023",

}

RIS

TY - GEN

T1 - Automated Human Facial Emotion Recognition System Using Depthwise Separable Convolutional Neural Network

AU - Hashan, Antor mahamudul

AU - Al-Saeedi adnan adhab, K

AU - Islam, Rizu md rakib ul

AU - Avinash, Kumar

AU - Dey, Subhankar

PY - 2023/7/13

Y1 - 2023/7/13

N2 - The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.

AB - The automatic human facial emotion recognition (AHFER) system has its wide significant contribution in several disciplines, such as human-computer collaboration, human-robot interaction, and so on. Multiple research projects have been conducted regarding this topic because it is a challenging and interesting task, especially in the area of computer vision. The purpose of the work is to recognize facial emotions using a depthwise separable convolutional neural network (DS-CNN). Apart from that, a facial emotion dataset has been proposed, and splitting functions, intensity normalization, image cropping, and grayscale conversion have been used in data pre-processing. The AHFER system is capable of recognizing four types of emotions: happy, sad, angry, and neutral. The results of the experiment showed that the AHFER method is 99 percent accurate when training and 93 percent accurate when validating. Additionally, we determined the confusion matrix with precision, recall, and fl-score. A comparison between the DS-CNN and DNN models was performed. The DS-CNN model performed significantly better than the DNN model. The DS-CNN model could be improved in the future by including more facial emotion categories.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85169425797

U2 - 10.1109/IAICT59002.2023.10205785

DO - 10.1109/IAICT59002.2023.10205785

M3 - Conference contribution

SN - 979-835031363-5

SP - 113

EP - 117

BT - Proceedings of the 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

Y2 - 13 July 2023 through 15 July 2023

ER -

ID: 44647263