Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

Phee Wei Qin; Ervin Gubin Moung; Ali Farzamnia; Farashazillah Yahya; John Julius Danker Khoo; Maisarah Mohd Sufian

doi:10.1109/ICCKE60553.2023.10326325

Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

Phee Wei Qin, Ervin Gubin Moung, Ali Farzamnia, Farashazillah Yahya, John Julius Danker Khoo, Maisarah Mohd Sufian

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

Abstract

Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learningbased facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score.

Original language	English
Title of host publication	2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	73-78
Number of pages	6
ISBN (Electronic)	9798350330151
ISBN (Print)	9798350330168
DOIs	https://doi.org/10.1109/ICCKE60553.2023.10326325
Publication status	Published - 27 Nov 2023
Externally published	Yes
Event	13th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of Duration: 1 Nov 2023 → 2 Nov 2023 Conference number: 13 https://iccke.um.ac.ir/2023

Publication series

Name	International Conference on Computer and Knowledge Engineering
Publisher	IEEE
Volume	2023
ISSN (Print)	2375-1304
ISSN (Electronic)	2643-279X

Conference

Conference	13th International Conference on Computer and Knowledge Engineering
Abbreviated title	ICCKE 2023
Country/Territory	Iran, Islamic Republic of
City	Mashhad
Period	1/11/23 → 2/11/23
Internet address	https://iccke.um.ac.ir/2023

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10.1109/ICCKE60553.2023.10326325Licence: Unspecified

Cite this

Qin, P. W., Moung, E. G., Farzamnia, A., Yahya, F., Khoo, J. J. D., & Sufian, M. M. (2023). Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis. In 2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023 (pp. 73-78). [10326325] (International Conference on Computer and Knowledge Engineering; Vol. 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCKE60553.2023.10326325

Qin, Phee Wei ; Moung, Ervin Gubin ; Farzamnia, Ali et al. / Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis. 2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 73-78 (International Conference on Computer and Knowledge Engineering).

@inproceedings{0232a491bf1846e4aa026a63611bee3a,

title = "Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis",

abstract = "Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learningbased facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score.",

keywords = "CNN, color space, Emotion, facial expression recognition, sentiment analysis",

author = "Qin, {Phee Wei} and Moung, {Ervin Gubin} and Ali Farzamnia and Farashazillah Yahya and Khoo, {John Julius Danker} and Sufian, {Maisarah Mohd}",

note = "Funding Information: ACKNOWLEDGMENT This research is supported by the Ministry of Higher Education Malaysia through Fundamental Research Grant Scheme (FRGS/1/2020/ICT06/UMS/02/1). Publisher Copyright: {\textcopyright} 2023 IEEE.; 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023 ; Conference date: 01-11-2023 Through 02-11-2023",

year = "2023",

month = nov,

day = "27",

doi = "10.1109/ICCKE60553.2023.10326325",

language = "English",

isbn = "9798350330168",

series = "International Conference on Computer and Knowledge Engineering",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023",

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url = "https://iccke.um.ac.ir/2023",

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Qin, PW, Moung, EG, Farzamnia, A, Yahya, F, Khoo, JJD & Sufian, MM 2023, Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis. in 2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023., 10326325, International Conference on Computer and Knowledge Engineering, vol. 2023, Institute of Electrical and Electronics Engineers Inc., pp. 73-78, 13th International Conference on Computer and Knowledge Engineering, Mashhad, Iran, Islamic Republic of, 1/11/23. https://doi.org/10.1109/ICCKE60553.2023.10326325

Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis. / Qin, Phee Wei; Moung, Ervin Gubin; Farzamnia, Ali et al.

2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 73-78 10326325 (International Conference on Computer and Knowledge Engineering; Vol. 2023).

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

TY - GEN

T1 - Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

AU - Qin, Phee Wei

AU - Moung, Ervin Gubin

AU - Farzamnia, Ali

AU - Yahya, Farashazillah

AU - Khoo, John Julius Danker

AU - Sufian, Maisarah Mohd

N1 - Conference code: 13

PY - 2023/11/27

Y1 - 2023/11/27

N2 - Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learningbased facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score.

AB - Automated facial expression recognition is popular for diagnosing mental diseases and interpreting human emotions. Image sentiment analysis helps incorporate feedback for improved product development. However, there's a lack of research on the effect of different color spaces on facial expression recognition performance. Thus, there are three objectives in this project, which are to develop a deep learningbased facial expression recognition system for image sentiment analysis. Second, to investigate the effect of different color spaces on facial expression recognition and select the best one. Lastly, to evaluate the accuracy of the system on the AffectNet dataset. The color spaces to be tested are RGB, YCBCR, HSV, XYZ, and YIQ. The methodology involves selecting the AffectNet dataset, pre-processing the images, building a Convolutional Neural Network model, and evaluating performance using metrics like accuracy, precision, recall, and F1-score.

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KW - color space

KW - Emotion

KW - facial expression recognition

KW - sentiment analysis

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DO - 10.1109/ICCKE60553.2023.10326325

M3 - Conference contribution

AN - SCOPUS:85179755359

SN - 9798350330168

T3 - International Conference on Computer and Knowledge Engineering

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EP - 78

BT - 2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 13th International Conference on Computer and Knowledge Engineering

Y2 - 1 November 2023 through 2 November 2023

ER -

Qin PW, Moung EG, Farzamnia A, Yahya F, Khoo JJD, Sufian MM. Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis. In 2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 73-78. 10326325. (International Conference on Computer and Knowledge Engineering). doi: 10.1109/ICCKE60553.2023.10326325

Performance Evaluation Study of Color Space Selection In Video Based Facial Expression Recognition Using Deep Neural Networks For Sentiment Analysis

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