employee
Ekaterinburg, Russian Federation
Ekaterinburg, Ekaterinburg, Russian Federation
Ekaterinburg, Ekaterinburg, Russian Federation
Ekaterinburg, Ekaterinburg, Russian Federation
Ekaterinburg, Ekaterinburg, Russian Federation
UDC 159.9
CSCSTI 12.09
The paper addresses the task of automated determination of the extroversion/introversion (E/I) dichotomy based on the analysis of static facial photographs; proposes and experimentally investigates an algorithm based on extracting facial geometric features using the MediaPipe FaceMesh framework and subsequent classification with the K-Nearest Neighbors (KNN) algorithm. The relative distances between key anatomical facial points are used as features, which minimize the influence of image scale and shooting conditions. The empirical basis of the study is an author’s dataset formed based on respondents’ photographs with verified test results using the MBTI methodology. The authors conduct a series of experiments with varying sets of features, classifier parameters, and image selection criteria. The work shows that the quality and standardization of photographs (frontal view, absence of facial expressions, makeup, and head turns) have a critical impact on recognition accuracy. The maximum classification accuracy achieved is approximately 72% on the selected sample. The results obtained confirm the statistically significant relationship between the morphological characteristics of the face and the E/I dichotomy.
extroversion, introversion, dataset, photo, MediaPipe
1. Naz A., Khan H.U., Bukhari A., Alshemaimri B., Daud A., Ramzan M. Machine and Deep Learning for Personality Traits Detection: A Comprehensive Survey and Open Research Challenges. Artificial Intelligence Review. 2025;58:239. DOIhttps://doi.org/10.1007/s10462-025-11245-3.
2. Capraro R.M., Capraro M.M. Myers-Briggs Type Indicator Score Reliability Across: Studies a Meta-Analytic Reliability Generalization Study. Educational and Psychological Measurement. 2002;62(4):590-602. DOIhttps://doi.org/10.1177/0013164402062004004.
3. Cae L., Liu X. Identifying Big Five Personality Traits Based on Facial Behavior Analysis. Frontiers in Public Health. 2022;10:1001828. DOIhttps://doi.org/10.3389/fpubh.2022.1001828.
4. Kachur A., Osin E., Davydov D., Shutilov K., Novokshonov K. Assessing the Big Five Personality Traits Using Real-Life Static Facial Images. Scientific Reports. 2020;10(1):8487. DOIhttps://doi.org/10.1038/s41598-020-65358-6.
5. Li J., Tian M., Fang H., Xu M., Li H., Liu J. Extraversion Predicts Individual Differences in Face Recognition. Communicative & Integrity Biology. 2010;3(4):295-298. DOIhttps://doi.org/10.4161/cib.3.4.12093.
6. Pittenger D.J. The Utility of the Myers-Briggs Type Indicator. Review of Educational Research 1993;63(4):467-488. DOIhttps://doi.org/10.3102/00346543063004467.
7. Salsabila G.D., Setiawan E.B. Semantic Approach for Big Five Personality Prediction on Twitter. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi). 2024;5(4):1-10. DOIhttps://doi.org/10.29207/resti.v5i4.3197.
8. Joksimovic S., Ifenthaler D., Marrone R., De Laat M. Siemens G. Opportunities of Artificial Intelligence for Supporting Complex Problem-Solving: Findings from a Scoping Review. Computers and Education: Artificial Intelligence. 2023;4:100138. DOIhttps://doi.org/10.1016/j.caeai.2023.100138.
9. Zhang B., Huang Y., Cui F., Zhang H., Shang J. PsyAttention: Psychological Attention Model for Personality Detection from Facial Images. In: Findings of the Association for Computational Linguistics: EMNLP 2023; 2023. p. 3398-3411. DOIhttps://doi.org/10.18653/v1/2023.findings-emnlp.222.
10. Polozov A.A., Shtark M.P., Polozova K.A., et al. Determining of Person’s Character Type by Convolutional Neural Network (Using the MMPI Methodology). Models, Systems, Networks in Economics, Technology, Nature and Society. 2023;1(45)149-163. DOIhttps://doi.org/10.21685/2227-8486-2023-1-10.
11. Polozov A.A. Psychological Portraits of a Sports Club Staff. Sports Psychologist. 2005;(3):6-9.
