research-article

The application of artificial intelligence in emotion detection: a study based on the effect of parenting style on micro-expression recognition ability of college students

Authors:

Xunbing ShenAuthors Info & Claims

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 137 - 141

https://doi.org/10.1145/3500931.3500956

Published: 22 December 2021 Publication History

Abstract

Accurate recognition of micro-expressions facilitates emotional communication, and correct capture of expressions is associated with deception detection. The combination of artificial intelligence and psychology can be widely used in various fields such as national life, agricultural production, product development, and garrison construction. This study analyzes and explores whether there is a correlation between parenting style and micro-expression recognition ability, which can provide a basis for subsequent artificial intelligence to detect emotions. This paper mainly uses offline collection of micro-expression recognition data and filling out questionnaires at the same time to collect data and conduct survey research. A total of 175 questionnaires were collected, of which 175 were valid. 110 data of micro-expression recognition were collected, of which 100 were valid. The results found that there was a significant correlation between the parenting style and micro-expression recognition ability of college students. Specifically, the rejection dimension of parents was significantly and negatively correlated with micro-expression recognition ability. Artificial intelligence has a wide range of fields, among which the combination of machine learning and expression recognition can effectively improve the possibility of screening out suspicious people in specific situations.

References

[1]

Wen Gao, Hui Jin. Analysis and recognition of facial expression images[J]. Chinese Journal of Computers, 1997(09): 782--789.

[2]

Hui Jin, Wen Gao. Human face and facial expression recognition system[J]. Chinese Journal of Computers, 2000(06):602--608.

[3]

Zhiliang Wang, Fengjun Chen, Weimin Xue. Overview of facial expression recognition methods[J]. Computer Applications and Software, 2003(12):63--66.

[4]

Qi Wu, Xunbing Shen, Xiaolan Fu. Research and application of micro-expression[J]. Advances in Psychological Science, 2010, 18(09):1359--1368.

[5]

Shuaishi Liu, Yantao Tian, Chuan Wan. Facial expression recognition method based on Gabor multi-directional feature fusion and block histogram[J]. Acta Automatica Sinica, 2011, 37(12): 1455--1463.

[6]

Hongbo Deng, Lianwen Jin. A facial expression recognition method based on local Gabor filter bank and PCA+LDA[J]. Journal of Image and Graphics, 2007(02): 322--329.

[7]

Jingxin Zhao, Jiliang Shen, Wenxin Zhang. The relationship between children's emotional understanding, pro-social behavior and peer acceptance [J]. Psychological Development and Education, 2006(01): 1--6.

[8]

Yuli Xue, Xia Mao, Ye Guo, Shanwei Lu. Research progress of facial expression recognition in human-computer interaction [J]. Journal of Image and Graphics, 2009, 14(05): 764--772.

[9]

Xiao Sun, Ting Pan, Fuji Ren. Facial expression recognition based on ROI-KNN convolutional neural network[J]. Acta Automatica Sinica, 2016, 42(06): 883--891.

[10]

Bin Jiang, Kebin Jia, Guosheng Yang. Research progress in facial expression recognition[J]. Computer Science, 2011, 38(04): 25--31.

[11]

Guanming Lu, Jiali He, Jingjie Yan, Haibo Li. A Convolutional Neural Network for Facial Expression Recognition[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2016, 36(01): 16--22.

[12]

Jingfu Ye, Yongzhao Zhan. Facial expression feature extraction based on Gabor wavelet transform [J]. Computer Engineering, 2005(15): 172--174.

[13]

Dan Meng. Research on image classification method based on deep learning [D]. East China Normal University, 2017.

[14]

Xue Sui, Yantao Ren. Real-time processing of facial expression recognition[J]. Psychological Bulletin, 2007(01): 64--70.

[15]

Liang Jing, Wenjing Yan, Qi Wu, Xunbing Shen, Sujing Wang, Xiaolan Fu. Progress and prospects of micro-expression research[J]. National Science Foundation of China, 2013, 27(02): 75--78+82.

[16]

Xiaomin Liu, Yujin Zhang. Facial expression recognition based on Gabor histogram features and MVBoost[J]. Computer Research and Development, 2007(07):1089--1096.

[17]

Dawei Wang, Jun Zhou, Hongyan Mei, Su Zhang. Overview of facial expression recognition[J]. Computer Engineering and Applications, 2014, 50(20): 149--157+181.

[18]

Xugan Shi. Facial expression recognition based on deep learning [D]. Zhejiang Sci-Tech University, 2015.

[19]

Xin Wang, Yousheng Wang. Overview of facial expression recognition based on deep learning and traditional machine learning [J]. Applied Science and Technology, 2018, 45(01): 65--72.

[20]

Jinxin Ruan. Research on Key Technologies of Multi-Pose Face Detection and Expression Recognition [D]. South China University of Technology, 2010.

[21]

Ming Yin, Jianxin Zhang, Aiqin Shi, Dianzhi Liu. Features, recognition, training and influencing factors of micro-expression [J]. Advances in Psychological Science, 2016, 24(11): 1723--1736.

[22]

Xunbing Shen, Huajie Sui, Xiaolan Fu. The application of micro-expression in deception detection[J]. Advances in Psychological Science, 2017, 25(02): 211--220.

[23]

Xiaolan Fu, Hui Wang, Wei Fan. Research on facial expression recognition of patients with depression[J]. Psychological and Behavioral Research, 2015, 13(05): 691--697+720.

[24]

Xiangzhen Chen. Research on facial expression recognition algorithm based on deep learning [D]. Shenyang University of Technology, 2016.

[25]

Jing Yan. A comparative study on the micro-expression recognition ability of different faces between the East and the West between middle school students and college students [D]. Tianjin Normal University, 2014.

[26]

Yao Liu. The effect of micro-expression recognition training on executive function of adolescent depression patients[D]. Shanxi Medical University, 2016.

[27]

Mingsai Sun. Correlation and experimental research on micro-expression recognition and emotional intelligence of college students[D]. Shanghai Normal University, 2016.

[28]

Yueting Yang. Research on the development of 4--6 year old children's micro expression recognition ability [D]. Shaanxi Normal University, 2016.

[29]

Xing Fan, Xunbing Shen, Jie Jiang, Tianxing Lian, Weina Ma. The correlation between the micro-expression recognition ability of college students and the 16 personality factors of cartel[J]. Journal of Hangzhou Normal University (Natural Science Edition), 2017, 16(02):130--135.

[30]

Shaomeng Zhang. A comparative study of executive function and microexpression recognition between adolescent depression patients and normal adolescents[D]. Shanxi Medical University, 2017.

Cited By

Prome SRagavan NIslam MAsirvatham DJegathesan A(2024)Deception detection using machine learning (ML) and deep learning (DL) techniques: A systematic reviewNatural Language Processing Journal10.1016/j.nlp.2024.1000576(100057)Online publication date: Mar-2024
https://doi.org/10.1016/j.nlp.2024.100057

Index Terms

The application of artificial intelligence in emotion detection: a study based on the effect of parenting style on micro-expression recognition ability of college students
1. General and reference
  1. Document types
    1. Surveys and overviews

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ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 2021

593 pages

ISBN:9781450395588

DOI:10.1145/3500931

Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 22 December 2021

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ISAIMS 2021

ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 29 - 31, 2021

Beijing, China

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Cited By

Prome SRagavan NIslam MAsirvatham DJegathesan A(2024)Deception detection using machine learning (ML) and deep learning (DL) techniques: A systematic reviewNatural Language Processing Journal10.1016/j.nlp.2024.1000576(100057)Online publication date: Mar-2024
https://doi.org/10.1016/j.nlp.2024.100057

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