Dynamics of Key Facial Points as an Indicator of the Credibility of Reported Information

This research describes a method for studying the authenticity/unauthenticity of the information re- ported by people in video images. It is based on automatic tracking of coordinates of key points of a speaker’s face using OpenFace software. When processing the data, the multiple linear regression procedure is used. It was found that the dynamics of neighboring key points in the obtained models has a multidirectional char- acter, indicating the presence of a superposition of several dynamic structures, corresponding to the characteristic complex changes in the face position and facial expressions of the sitter. Their isolation is realized by means of the principal component analysis. It is shown, that the first 11 principal components describe 99.7% of the variability of the initial data. The correlation analysis between the number of credibility/confidence statements on the set of time intervals and the principal component loadings, allows to differentiate the dynamic structures of the face, connected with the assessments of credibility of the reported information. Automated analysis of face dynamics optimizes the process of collecting empirical data on the sitter’s appearance and their semantic structuring, as well as expands the range of predictors of the assessments of the truthfulness of the messages received.

1. Barabanschikov V.A., Zhegallo A.V. Okulomotor activity at the perception of dynamic and static expressions of the face. Eksperimental’naâ psihologiâ = Experimental Psychology (Russia), 2018. Vol. 11, no. 1, pp. 5—34. DOI:10.17759/exppsy.2018110101. (In Russ., аbstr. in Engl.).
2. Barabanschikov V.A., Zhegallo A.V., Khoze E.G., Solomonova A.V. Nonverbal predictors in the estimates of truthful and deceptive statements. Eksperimental’naâ psihologiâ = Experimental Psychology (Russia), 2018. Vol. 11, no. 4, pp. 94—106. DOI:10.17759/exppsy.2018110408. (In Russ., аbstr. in Engl.).
3. Barabanschikov V. A., Nosulenko V. N., Samoilenko E. S., Khoze E. G. Polipozitsionnoe nablyudenie za povedeniem lyudei v protsesse tselenapravlennoi strukturirovannoi besedy = Polypositional observation of human behavior in the process of purposeful structured conversation // Psychology and Pedagogy of the XXI century: theory, practice and prospects / N.B. Karabushchenko, N.P. Sungurova. Moscow. RUDN, 2015. P. 109-131. (In Russ.).
4. Baltrušaitis T. Automatic facial expression analysis // Submitted on March, 2014 for the Degree of Doctor of Philosophy. Fitzwilliam College. University of Cambridge.
5. Baltrušaitis T., Zadeh A., Lim Y.C., Morency Y-P. OpenFace 2.0: Facial Behavior Analysis Toolkit // Proc. IEEE International Conference on Automatic Face and Gesture Recognition. 15—19 May 2018. Xi’an, China, 2018. (FG ’18). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018. P. 59—66. DOI: 10.1109/FG.2018.00019
6. Hastie T.J., Pregibon D. Chapter 6. Generalized linear models // Statistical Models in S / eds Chambers J.M., Hastie T.J. London: Chapman & Hall, 1992. P. 195—248.
7. R Core Team. R: A language and environment for statistical computing [Электронный ресурс] // R Foundation for Statistical Computing. Vienna, Austria, 2020. URL: https://www.R-project. org/ (Accessed: 07.06.2021).