The Problem of Identifying a Person in a Face Masking Situation

127

Abstract

The article is dedicated to studying of psychological problems of recognizing a person by his/her face during the investigative action of presenting for identification. According to the result of the theoretical analysis it was found that the problem of recognizing a person in conditions of intentional face masking has not been practically studied despite of a number of research works on the problem of identifying a person by his/her face. An empirical study was conducted on a sample of young people at the age of 18-20 years old in the city of Yelets. Three hundred people participated in three stages of the experiment. The two types of images were used as motivation: a real appearance and an appearance with elements of disguise in which it was difficult to recognize the original face. The two initial hypotheses were proved: 1) the possibility of identification in a situation of intentional face masking depends on the preliminary installation of the recognizer; 2) the most effective installation during identifying of a person in a situation of intentional face masking is to fix the signs of the upper part of the face and to generalize psychological image of an identifiable person. Both hypotheses were successfully proved.

General Information

Keywords: face recognition, recognition, occlusion, face masking, holistic type of perception

Journal rubric: Interdisciplinary Studies

Article type: scientific article

DOI: https://doi.org/10.17759/psylaw.2023130115

Received: 02.01.2022

Accepted:

For citation: Budyakova T.P. The Problem of Identifying a Person in a Face Masking Situation [Elektronnyi resurs]. Psikhologiya i pravo = Psychology and Law, 2023. Vol. 13, no. 1, pp. 207–220. DOI: 10.17759/psylaw.2023130115. (In Russ., аbstr. in Engl.)

References

  1. Barabanshchikov V.A., Nosulenko V.N. Sistemnost’. Vospriyatie. Obshchenie. Moscow: IP RAN Publ., 2004. 480 p. (In Russ.).
  2. Bondarenko Ya.A., Menshikova G.Ya. Issledovanie roli analiticheskogo i kholisticheskogo protsessov v raspoznavanii litsevykh ekspressii [Exploring analytical and holistic processing in facial expression recognition] [Elektronnyi resurs]. Vestnik Moskovskogo universiteta. Seriya 14: Psikhologiya = Moscow University Psychology Bulletin, 2020, no. 2, pp. 103–140. doi:10.11621/vsp.2020.02.06 (In Russ.).
  3. Budyakova T.P. Psikhologicheskie oshibki pri opoznanii cheloveka po litsu [Psychological errors in the identification of a human face] [Elektronnyi resurs]. Eksperimental’naya psikhologiya = Experimental Psychology, 2017. Vol. 10, no. 2, pp. 20–39. doi:10.17759/exppsy.2017100203 (In Russ.).
  4. Budyakova T.P. Eksperimental’naya otsenka effektivnosti sistemy slovesnogo portreta pri opoznanii lichnosti [Experimental evaluation system in verbal portrait of personal identification] [Elektronnyi resurs]. Eksperimental’naya psikhologiya = Experimental Psychology, 2016. Vol. 9, no. 2, pp. 53–65. doi:10.17759/exppsy.2016090205 (In Russ.).
  5. Budyakova T.P. Effekt dezinformatsii v opoznanii cheloveka [Disinformation effect in human identification] [Elektronnyi resurs]. Psikhologiya i pravo = Psychology and Law, 2018. Vol. 8, no. 4, pp. 99–114. doi:10.17759/psylaw.2018080410 (In Russ.).
  6. Lupenko E.A. Vliyanie okklyuzii na vospriyatie i opoznanie lichnosti cheloveka, izobrazhennogo na portrete [The effect of occlusion on the perception and recognition of the identity of the person depicted in the portrait] [Elektronnyi resurs]. Eksperimental’naya psikhologiya = Experimental Psychology, 2014. Vol. 7, no. 1, pp. 44–55. URL: https://psyjournals.ru/journals/exppsy/archive/2014_n1/68177 (Accessed 29.12.2021). (In Russ.).
  7. Meshcheryakov B.G., Nazarov A.I., Chesnokova L.G., Yushchenkova D.V. Novaya popytka otkryt’ skrytoe opoznanie lits [A new attempt to discover the covert recognition of faces] [Elektronnyi resurs]. Eksperimental’naya psikhologiya = Experimental Psychology, 2015. Vol. 8, no. 4, pp. 45–60. doi:10.17759/exppsy.2015080404 (In Russ.).
  8. Mitrokhin V.K. Kriminalisticheskaya gabitoskopiya (ustanovlenie lichnosti po priznakam vneshnosti): Uchebnoe posobie. Ch. 2. Yuzhno-Sakhalinsk: SakhGU Publ., 2011. 116 p. (In Russ.).
  9. Stelmakh V.Yu. Doznanie v organakh vnutrennikh del: kurs lektsii. Yekaterinburg, 2015. 262 s. (In Russ.).
  10. Yushchenkova D.V., Meshcheryakov B.G. Raspoznavanie otdel’nykh chert litsa kak osnova uznavaniya tselogo litsa [Recognition of individual facial features as a basis for identification of the whole face] [Elektronnyi resurs]. Eksperimental’naya psikhologiya = Experimental Psychology, 2010. Vol. 3, no. 3, pp. 84–92. URL: https://psyjournals.ru/journals/exppsy/archive/2010_n3/32128 (Accessed 29.12.2021).
  11. Bah S.М., Ming F. An improved face recognition algorithm and its application in attendance management system. Array, 2020. Vol. 5. doi:10.1016/j.array.2019.100014
  12. Belanova E., Davis J.P., Thompson T. Cognitive and Neural Markers of Super-Recognisers’ Face Processing Superiority and Enhanced Cross-Age Effect. Cortex, 2018. Vol. 108, no. 11, pp. 92–111. doi:10.1016/j.cortex.2018.07.008
  13. Elmahmudi A., Ugail H. Deep face recognition using imperfect facial. Future Generation Computer Systems, 2019. Vol. 99, pp. 213–225. doi:10.1016/j.future.2019.04.025
  14. Frowd C.D., Hancock P., Bruce V. Et al. Catching more offenders with Evofit Facial Composites: Lab Research and Police Field Trials. Global Journal of Human Social Science, 2011. Vol. 11, no. 3, pp. 34–46.
  15. Jayaraman U., Gupta P., Gupta S., Arora G., Tiwari K. Recent development in face recognition. Neurocomputing, 2020. Vol. 408, pp. 231–245. doi:10.1016/j.neucom.2019.08.110
  16. Jeevan G., Zacharias G.C., Nair M.S., Rajan J. An empirical study of the impact of masks on face recognition. Pattern Recognition, 2022. Vol. 122. doi:10.1016/j.patcog.2021.108308
  17. Karimi-Rouzbahani H., Ramezani F., Woolgar A., Rich A., Ghodrati M. Perceptual difficulty modulates the direction of information flow in familiar face recognition. NeuroImage, 2021. Vol. 233, pp. 117896. doi:10.1016/j.neuroimage.2021.117896
  18. Kotsoglou K.N., Oswald M. The long arm of the algorithm? Automated Facial Recognition as evidence and trigger for police intervention. Forensic Science International: Synergy, 2020. Vol. 2, pp. 86–89. doi:10.1016/j.fsisyn.2020.01.002
  19. Lampinen J.M., Curry С.R., Erickson W.B. Prospective Person Memory: The Role of Self-Efficacy, Personal Interaction, and Multiple Images in Recognition of Wanted Persons. Journal of Police and Criminal Psychology, 2016. Vol. 31, no. 1, pp. 59–70. doi:10.1007/s11896-015-9164-7
  20. Ouanan H., Ouanan M., Aksasse B. Non-linear dictionary representation of deep features for face recognition from a single sample per person. Procedia Computer Science, 2018. Vol. 127, pp. 114–122. doi:10.1016/j.procs.2018.01.105
  21. Ramon M., Bobak A.K., White D. Super‐recognizers: From the lab to the world and back again. British Journal of Psychology, 2019. Vol. 110, no. 3, pp. 461–479. doi:10.1111/bjop.12368
  22. Rollins L., Olsen A., Evans M. Social categorization modulates own-age bias in face recognition and ERP correlates of face processing. Neuropsychologia, 2020. Vol. 141. doi:10.1016/j.neuropsychologia.2020.107417
  23. Saraiva R.B., Boeijen I.V., Hope L. et al. Eyewitness metamemory predicts identification performance in biased and unbiased line‐ Legal and Criminological Psychology, 2020. Vol. 3. doi:10.1111/lcrp.121
  24. Singh R., Vatsa M., Noore A. Face recognition with disguise and single gallery images. Image and Vision Computing, 2009. Vol. 27, no. 3, pp. 245–257. doi:10.1016/j.imavis.2007.06.010
  25. Villalba G. et al. A PRNU-based counter-forensic method to manipulate smartphone image source identification techniques. Future Generation Computer Systems — The International Journal of e-Science, 2017. Vol. 76, pp. 418–427. doi:10.1016/j.future.2016.11.007
  26. Vinay A. et al. Two Dimensionality Reduction Techniques for SURF Based Face Recognition. Procedia Computer Science, 2016. Vol. 85, pp. 241–248. doi:10.1016/j.procs.2016.05.222
  27. Wang X., Zhang W. Anti-occlusion face recognition algorithm based on a deep convolutional neural network. Computers & Electrical Engineering, 2021. Vol. 96(A). doi:10.1016/j.compeleceng.2021.107461
  28. Yoshino M. et al. Individual identification of disguised faces by morphometrical matching. Forensic Science International, 2002. Vol. 127, no. 1-2, pp. 97–103. doi:10.1016/S0379-0738(02)00115-9
  29. Yang Q., Wang P., Fang Z., Lu Q. Focus on the Visible Regions: Semantic-Guided Alignment Model for Occluded Person Re-Identification. Sensors, 2020. Vol. 20, no. 16. doi:10.3390/s20164431
  30. Zhao C., Li X., Dong Y. Learning blur invariant binary descriptor for face recognition. Neurocomputing, 2020. Vol. 40–43, pp. 34–40. doi:10.1016/j.neucom.2020.04.082

Information About the Authors

Tatiana P. Budyakova, PhD in Psychology, Docent, Professor, Department of Pedagogy & Educational Technologies, Institute of Psychology & Pedagogy, Bunin Yelets State University, Yelets, Russia, ORCID: https://orcid.org/0000-0003-1739-837X, e-mail: budyakovaelez@mail.ru

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