Analyzing and evaluating personality and human behavior based on facial index and big five model

Numerous studies have shown that morphological and social indicators in a human face can provide information about a person's personality and behavior. The Big Five model, also known as the Five-Factor, is the five basic dimensions of personality. These dimensions include openness, conscientiousness, extraversion, agreeableness and neuroticism. The Big Five model has been applied in a variety of different settings, including clinical psychology, organizational psychology, and even marketing research. By examining where an individual falls on each of these dimensions, researchers can gain insight into their unique personality traits and use this information to make predictions about their behavior and performance in different situations. In our existing iscv platform, a job searching website, we can help employers better understanding employee incentives by utilizing the personality traits information of candidates. Managers and CEOs can therefore discover a means to improve relationships and communication while also managing and building teams more effectively. We trained a machine learning model using a hybrid CNN-LSTM, ResNet, VGG19 algorithm for personality recognition through interview video. In each video, we analyze facial movement by using the 3D landmarks extracted with the 3DDFA-V2 algorithm. The model uses the UDIVA v0.5 dataset, collected in the scope of the research project entitled “Understanding Face-to-Face Dyadic Interactions through Social Signal Processing”. The experimental results conclude: (i) Analyzing facial movement by using the 3D landmarks extracted with the 3DDFA-V2 algorithm. (ii) Personality traits inferred from facial behaviors by most benchmarked deep learning model. (iii) Personality assessment model is trained from a combination of two data sets (one UDIVIA dataset and one self-survey dataset) to fit Asian personalities. (iv) The detailed Big Five personality tendency assessment table is based on the interview video and questionnaire of the surveyed people.

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Publication Details

Journal
Journal of Computer Science and Cybernetics
Published
2024-09-21
DOI
https://doi.org/10.15625/1813-9663/18677
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
0.00

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article

Analyzing and evaluating personality and human behavior based on facial index and big five model

Anh Le, Quan Nguyen Minh, Thuan Dinh Nguyen, Minh Nhut Nguyen et al.
Journal of Computer Science and Cybernetics
Image and Video Quality Assessment
article

Analyzing and evaluating personality and human behavior based on facial index and big five model

Anh Le, Quan Nguyen Minh, Thuan Dinh Nguyen, Minh Nhut Nguyen, Nam Do Dang Kien
article en

Abstract

Numerous studies have shown that morphological and social indicators in a human face can provide information about a person's personality and behavior. The Big Five model, also known as the Five-Factor, is the five basic dimensions of personality. These dimensions include openness, conscientiousness, extraversion, agreeableness and neuroticism. The Big Five model has been applied in a variety of different settings, including clinical psychology, organizational psychology, and even marketing research. By examining where an individual falls on each of these dimensions, researchers can gain insight into their unique personality traits and use this information to make predictions about their behavior and performance in different situations. In our existing iscv platform, a job searching website, we can help employers better understanding employee incentives by utilizing the personality traits information of candidates. Managers and CEOs can therefore discover a means to improve relationships and communication while also managing and building teams more effectively. We trained a machine learning model using a hybrid CNN-LSTM, ResNet, VGG19 algorithm for personality recognition through interview video. In each video, we analyze facial movement by using the 3D landmarks extracted with the 3DDFA-V2 algorithm. The model uses the UDIVA v0.5 dataset, collected in the scope of the research project entitled “Understanding Face-to-Face Dyadic Interactions through Social Signal Processing”. The experimental results conclude: (i) Analyzing facial movement by using the 3D landmarks extracted with the 3DDFA-V2 algorithm. (ii) Personality traits inferred from facial behaviors by most benchmarked deep learning model. (iii) Personality assessment model is trained from a combination of two data sets (one UDIVIA dataset and one self-survey dataset) to fit Asian personalities. (iv) The detailed Big Five personality tendency assessment table is based on the interview video and questionnaire of the surveyed people.

Journal of Computer Science and CyberneticsVol. 40(3)
Vietnam National University Ho Chi Minh City (VN), Ho Chi Minh City University of Science (VN), Ho Chi Minh City University of Technology (VN)
Viet Nam National University Ho Chi Minh City
Openalex Percentile: Top 71%
Image and Video Quality Assessment
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