AI-generated faces show lower morphological diversity than real faces do

Olga Boudníková; Karel Kleisner

doi:10.18778/1898-6773.87.1.06

Authors

Olga Boudníková Charles University, Faculty of Science, Department of Philosophy and History of Science https://orcid.org/0009-0001-1333-5198
Karel Kleisner Charles University, Faculty of Science, Department of Philosophy and History of Science

DOI:

https://doi.org/10.18778/1898-6773.87.1.06

Keywords:

geometric morphometrics, GAN, artificial intelligence, human face, morphology, symmetry

Abstract

Some recent studies suggest that artificial intelligence can create realistic human faces subjectively unrecognizable from faces of real people. We have compared static facial photographs of 197 real men with a sample of 200 male faces generated by artificial intelligence to test whether they converge in basic morphological characteristic such as shape variation and bilateral asymmetry. Both datasets depicted standardized faces of European men with a neutral expression. Then we used geometric morphometrics to investigate their facial morphology and calculate the measures of shape variation and asymmetry. We found that the natural faces of real individuals were more variable in their facial shape than the artificially generated faces were. Moreover, the artificially synthesized faces showed lower levels of facial asymmetry than the control group. Despite the rapid development of generative adversarial networks, natural faces are thus still statistically distinguishable from the artificial ones by objective measurements. We recommend the researchers in face perception, that aim to use artificially generated faces as ecologically valid stimuli, to check whether their stimuli morphological variance is comparable with that of natural faces in a target population.

Downloads

Download data is not yet available.

References

Adams DC, Otárola-Castillo E. 2013. Geomorph: An r Package for the Collection and Analysis of Geometric Morphometric Shape Data. Methods in Ecology and Evolution 4(4):393–99. https://doi.org/10.1111/2041-210X.12035
View in Google Scholar DOI: https://doi.org/10.1111/2041-210X.12035

Adams D, Collyer M, Kaliontzopoulou A, Baken E. 2023. Geomorph: Geometric Morphometric Analyses of 2D and 3D Landmark Data. Available at: https://cran.r-project.org/web/packages/geomorph/geomorph.pdf
View in Google Scholar

Anantrasirichai N, Bull D. 2022. Artificial Intelligence in the Creative Industries: A Review. Artificial Intelligence Review 55(1):589–656. https://doi.org/10.1007/s10462-021-10039-7
View in Google Scholar DOI: https://doi.org/10.1007/s10462-021-10039-7

Baken E, Collyer M, Kaliontzopoulou A, Adams D. 2021. Geomorph v4.0 and gmShiny: Enhanced Analytics and a New Graphical Interface for a Comprehensive Morphometric Experience. Methods in Ecology and Evolution 12. https://doi.org/10.1111/2041-210x.13723
View in Google Scholar DOI: https://doi.org/10.1111/2041-210X.13723

Bray SD, Johnson SD, Kleinberg B. 2023. Testing Human Ability to Detect “Deepfake” Images of Human Faces. Journal of Cybersecurity 9(1):tyad011. https://doi.org/10.1093/cybsec/tyad011
View in Google Scholar DOI: https://doi.org/10.1093/cybsec/tyad011

Cetinic E, She J. 2022. Understanding and Creating Art with AI: Review and Outlook. ACM Transactions on Multimedia Computing, Communications, and Applications 18(2):66:1-66:22. https://doi.org/10.1145/3475799
View in Google Scholar DOI: https://doi.org/10.1145/3475799

Courset R, Rougier M, Palluel-Germain R, Smeding A, Manto Jonte J, Chauvin A, et al. 2018. The Caucasian and North African French Faces (CaNAFF): A Face Database. 31(1):22. https://doi.org/10.5334/irsp.179
View in Google Scholar DOI: https://doi.org/10.5334/irsp.179

Geller Tom. 2008. Overcoming the Uncanny Valley. IEEE Computer Graphics and Applications 28(4):11–17. https://doi.org/10.1109/MCG.2008.79
View in Google Scholar DOI: https://doi.org/10.1109/MCG.2008.79

Karras T, Laine S, Aila T. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. Available at: https://openaccess.thecvf.com/content_CVPR_2019/papers/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.pdf
View in Google Scholar DOI: https://doi.org/10.1109/CVPR.2019.00453

Kleisner K. 2021. Morphological Uniqueness: The Concept and Its Relationship to Indicators of Biological Quality of Human Faces from Equatorial Africa. Symmetry 13(12):2408. https://doi.org/10.3390/sym13122408
View in Google Scholar DOI: https://doi.org/10.3390/sym13122408

Kleisner K, Pokorný Š, Saribay SA. 2019. Toward a New Approach to Cross-Cultural Distinctiveness and Typicality of Human Faces: The Cross-Group Typicality/Distinctiveness Metric. Frontiers in Psychology 10. https://doi.org/10.3389/fpsyg.2019.00124
View in Google Scholar DOI: https://doi.org/10.3389/fpsyg.2019.00124

Kleisner K, Tureček P, Roberts SC, Havlíček J, Valentova JV, Akoko RB, et al. 2021. How and Why Patterns of Sexual Dimorphism in Human Faces Vary across the World. Scientific Reports 11(1):5978. https://doi.org/10.1038/s41598-021-85402-3
View in Google Scholar DOI: https://doi.org/10.1038/s41598-021-85402-3

Kleisner K, Tureček P, Saribay S, Pavlovič O, Leongómez J, Roberts S, et al. 2023. Distinctiveness and Femininity, Rather than Symmetry and Masculinity, Affect Facial Attractiveness across the World. Evolution and Human Behavior. https://doi.org/10.1016/j.evolhumbehav.2023.10.001
View in Google Scholar DOI: https://doi.org/10.1016/j.evolhumbehav.2023.10.001

Lago F, Pasquini C, Böhme R, Dumont H, Goffaux V, Boato G. 2022. More Real Than Real: A Study on Human Visual Perception of Synthetic Faces [Applications Corner]. IEEE Signal Processing Magazine 39(1):109–16. https://doi.org/10.1109/MSP.2021.3120982
View in Google Scholar DOI: https://doi.org/10.1109/MSP.2021.3120982

Lakshmi A, Wittenbrink B, Correll J, Ma DS. 2021. The India Face Set: International and Cultural Boundaries Impact Face Impressions and Perceptions of Category Membership. Frontiers in Psychology 12. https://doi.org/10.3389/fpsyg.2021.627678
View in Google Scholar DOI: https://doi.org/10.3389/fpsyg.2021.627678

Liefooghe B, Oliveira M, Leisten LM, Hoogers E, Aarts H, Hortensius R. 2023. Are Natural Faces Merely Labelled as Artificial Trusted Less? Collabra: Psychology 9(1):73066. https://doi.org/10.1525/collabra.73066
View in Google Scholar DOI: https://doi.org/10.1525/collabra.73066

Linke LS, Saribay A, Kleisner K. 2016. Perceived Trustworthiness Is Associated with Position in a Corporate Hierarchy. Personality and Individual Differences 99:22–27. https://doi.org/10.1016/j.paid.2016.04.076
View in Google Scholar DOI: https://doi.org/10.1016/j.paid.2016.04.076

Ma DS, Correll J, Wittenbrink B. 2015. The Chicago Face Database: A Free Stimulus Set of Faces and Norming Data. Behavior Research Methods 47(4):1122–35. https://doi.org/10.3758/s13428-014-0532-5
View in Google Scholar DOI: https://doi.org/10.3758/s13428-014-0532-5

Marcus G, Davis E, Aaronson S. 2022. A Very Preliminary Analysis of DALL-E 2. Available at: https://www.researchgate.net/publication/360311114_A_very_preliminary_analysis_of_DALL-E_2
View in Google Scholar

Miller EJ, Steward BA, Witkower Z, Sutherland CAM, Krumhuber EG, Dawel A. 2023. AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones. Psychological Science 34(12):1390–1403. https://doi.org/10.1177/09567976231207095
View in Google Scholar DOI: https://doi.org/10.1177/09567976231207095

Moshel ML, Robinson AK, Carlson TA, Grootswagers T. 2022. Are You for Real? Decoding Realistic AI-Generated Faces from Neural Activity. Vision Research 199:108079. https://doi.org/10.1016/j.visres.2022.108079
View in Google Scholar DOI: https://doi.org/10.1016/j.visres.2022.108079

Mustak M, Salminen J, Mäntymäki M, Rahman A, Dwivedi YK. 2023. Deepfakes: Deceptions, Mitigations, and Opportunities. Journal of Business Research 154:113368. https://doi.org/10.1016/j.jbusres.2022.113368
View in Google Scholar DOI: https://doi.org/10.1016/j.jbusres.2022.113368

Nightingale SJ, Farid H. 2022. AI-Synthesized Faces Are Indistinguishable from Real Faces and More Trustworthy. Proceedings of the National Academy of Sciences 119(8):e2120481119. https://doi.org/10.1073/pnas.2120481119
View in Google Scholar DOI: https://doi.org/10.1073/pnas.2120481119

Pasquini C, Laiti F, Lobba D, Ambrosi G, Boato G, De Natale F. 2023. Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis. Applied Sciences 13(2):816. https://doi.org/10.3390/app13020816
View in Google Scholar DOI: https://doi.org/10.3390/app13020816

Rossi S, Kwon Y, Auglend OH, Mukkamala RR, Rossi M, Thatcher J. 2022. Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles? Available at: https://www.researchgate.net/publication/363584758_Are_Deep_Learning-Generated_Social_Media_Profiles_Indistinguishable_from_Real_Profiles
View in Google Scholar DOI: https://doi.org/10.24251/HICSS.2023.017

Saribay SA, Biten AF, Meral EO, Aldan P, Třebický V, Kleisner K. 2018. The Bogazici Face Database: Standardized Photographs of Turkish Faces with Supporting Materials. PLOS ONE 13(2):e0192018. https://doi.org/10.1371/journal.pone.0192018.
View in Google Scholar DOI: https://doi.org/10.1371/journal.pone.0192018

Schlager S. 2017. Chapter 9 – Morpho and Rvcg – Shape Analysis in R: R-Packages for Geometric Morphometrics, Shape Analysis and Surface Manipulations. In: G. Zheng, S. Li, and G. Székely. Statistical Shape and Deformation Analysis. Academic Press. Pp. 217–56.
View in Google Scholar DOI: https://doi.org/10.1016/B978-0-12-810493-4.00011-0

Sofer C, Dotsch R, Wigboldus DHJ, Todorov A. 2015. What Is Typical Is Good: The Influence of Face Typicality on Perceived Trustworthiness. Psychological Science 26(1):39–47. https://doi.org/10.1177/0956797614554955
View in Google Scholar DOI: https://doi.org/10.1177/0956797614554955

Tucciarelli R, Vehar N, Chandaria S, Tsakiris M. 2022. On the Realness of People Who Do Not Exist: The Social Processing of Artificial Faces. iScience 25(12):105441. https://doi.org/10.1016/j.isci.2022.105441
View in Google Scholar DOI: https://doi.org/10.1016/j.isci.2022.105441

Wong AD. 2022. BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN Faces. https://doi.org/10.48550/arXiv.2210.06587
View in Google Scholar