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Zero-shot Personality Perception From Facial Images

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

Personality perception is an important process that affects our behaviours towards others, with applications across many domains. Automatic personality perception (APP) tools can help create more natural interactions between humans and machines, and better understand human-human interactions. However, collecting personality assessments is a costly and tedious task. This paper presents a new method for zero-shot facial image personality perception tasks. Harnessing the latent psychometric layer of CLIP (Contrastive Language-Image Pre-training), the proposed PsyCLIP is the first zero-shot personality perception model achieving competitive results, compared to state-of-the-art supervised models. With PsyCLIP, we establish the existence of latent psychometric information in CLIP and demonstrate its use in the domain of personality computing. For evaluation, we compiled a new personality dataset consisting of 41800 facial images of various individuals labelled with their corresponding perceived Myers Briggs Type Indicator (MBTI) types. PsyCLIP achieved statistically significant results (p<0.01) in predicting all four Myers Briggs dimensions without requiring any training dataset.

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References

  1. Engines - openai api. https://beta.openai.com/docs/engines/gpt-3. Accessed 22 Apr 2022

  2. The ethics of artificial intelligence: Issues and initiatives: Think tank: European parliament (2020). https://europarl.europa.eu/thinktank/en/document/EPRS_STU634452

  3. Personality database. www.personality-database.com/vote (2022). Accessed 06 Jan 2022

  4. Ali, S., Parikh, D.: Telling creative stories using generative visual aids. arXiv preprint arXiv:2110.14810 (2021)

  5. Biel, J.I., Teijeiro-Mosquera, L., Gatica-Perez, D.: Facetube: predicting personality from facial expressions of emotion in online conversational video. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 53–56 (2012)

    Google Scholar 

  6. Borkenau, P., Brecke, S., Möttig, C., Paelecke, M.: Extraversion is accurately perceived after a 50-ms exposure to a face. J. Res. Pers. J. Res. Pers. 43(4), 703–706 (2009). https://doi.org/10.1016/j.jrp.2009.03.007

    Article  Google Scholar 

  7. Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis (2019)

    Google Scholar 

  8. Brown, T.B., et al.: Language Models are Few-Shot Learners. arXiv (2020). arxiv.org/2005.14165v4

  9. Celli, F., Lepri, B.: Is big five better than mbti? a personality computing challenge using twitter data. In: CLiC-it (2018)

    Google Scholar 

  10. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248–255 (2009)

    Google Scholar 

  11. Deng, L.: The mnist database of handwritten digit images for machine learning research. IEEE Sign. Process. Mag. 29(6), 141–142 (2012)

    Article  Google Scholar 

  12. Durupinar, F.: Personality-Driven Gaze Animation with Conditional Generative Adversarial Networks. arXiv (2020). arxiv.org/2012.02224v1

  13. Farnadi, Get al.: Computational personality recognition in social media. User Model. User-Adapt. Interact. 26(2–3), 109–142 (2016)

    Google Scholar 

  14. Furnham, A.: The of big five versus the big four: the relationship between the myers-briggs type indicator (mbti) and neo-pi five factor model personality. Pers. Individ. Differ 21(2), 303–307 (1996)

    Article  Google Scholar 

  15. Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014)

    Google Scholar 

  16. Gürpınar, F., Kaya, H., Salah, A.A.: Combining deep facial and ambient features for first impression estimation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 372–385. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_30

    Chapter  Google Scholar 

  17. Hermann, I.: Artificial intelligence in fiction: between narratives and metaphors. AI Soc. 1–11 (2021)

    Google Scholar 

  18. Junior, J.C.S.J., et al.: First impressions: a survey on vision-based apparent personality trait analysis. IEEE Trans. Affect. Comput. 1 (2019). https://doi.org/10.1109/TAFFC.2019.2930058

  19. Kachur, A., Osin, E., Davydov, D., Shutilov, K., Novokshonov, A.: Assessing the big five personality traits using real-life static facial images. Sci. Rep. 10(8487), 1–11 (2020). https://doi.org/10.1038/s41598-020-65358-6

    Article  Google Scholar 

  20. Kenny, D.A.: Person: a general model of interpersonal perception. Pers. Soc. Psychol. Rev. 8(3), 265–280 (2004)

    Article  Google Scholar 

  21. Kramer, R.S., King, J.E., Ward, R.: Identifying personality from the static, nonexpressive face in humans and chimpanzees: evidence of a shared system for signaling personality. Evol. Hum. Behav. 32(3), 179–185 (2011)

    Article  Google Scholar 

  22. Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128(2), 261–318 (2019). https://doi.org/10.1007/s11263-019-01247-4

  23. lucidrain: Github - lucidrains/big-sleep. http://github.com/lucidrains/big-sleep (2021). Accessed 06 Jan 2022

  24. lucidrain: Github - lucidrains/deep-daze. http://github.com/lucidrains/deep-daze (2021). Accessed 06 Jan 2022

  25. Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)

    Google Scholar 

  26. Mohammadi, G., Vinciarelli, A.: Automatic personality perception: prediction of trait attribution based on prosodic features. IEEE Trans. Affect. Comput. 3(3), 273–284 (2012)

    Article  Google Scholar 

  27. Mohammadi, G., Vinciarelli, A.: Automatic personality perception: prediction of trait attribution based on prosodic features extended abstract. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 484–490. IEEE (2015)

    Google Scholar 

  28. Mohammadi, G., Vuilleumier, P.: A multi-componential approach to emotion recognition and the effect of personality. IEEE Trans. Affect. Comput. (2020)

    Google Scholar 

  29. Myers, I.B.: The myers-briggs type indicator: Manual (1962) (1962)

    Google Scholar 

  30. Naumann, L.P., Vazire, S., Rentfrow, P.J., Gosling, S.D.: Personality judgments based on physical appearance. Pers. Soc. Psychol. Bull. 35(12), 1661–1671 (2009). https://doi.org/10.1177/0146167209346309

    Article  Google Scholar 

  31. Oosterhof, N.N., Todorov, A.: The functional basis of face evaluation. Proc. Natl. Acad. Sci. 105(32), 11087–11092 (2008)

    Article  Google Scholar 

  32. Patil, S.M., Singh, R., Patil, P., Pathare, N.: Personality prediction using digital footprints. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1736–1742. IEEE (2021)

    Google Scholar 

  33. Radford, A., et al.: Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 (2021)

  34. Ruane, E., Farrell, S., Ventresque, A.: User Perception of Text-Based Chatbot Personality. In: Følstad, A., Araujo, T., Papadopoulos, S., Law, E.L.-C., Luger, E., Goodwin, M., Brandtzaeg, P.B. (eds.) CONVERSATIONS 2020. LNCS, vol. 12604, pp. 32–47. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68288-0_3

    Chapter  Google Scholar 

  35. Salgado, J.F.: The big five personality dimensions and counterproductive behaviors. Int. J. Select. Assess. 10(1–2), 117–125 (2002)

    Article  Google Scholar 

  36. Shevlin, M., Walker, S., Davies, M., Banyard, P., Lewis, C.A.: Can you judge a book by its cover? evidence of self-stranger agreement on personality at zero acquaintance. Pergamon-Elsevier (2003). http://irep.ntu.ac.uk/id/eprint/16819

  37. Sijtsma, K.: Introduction to the measurement of psychological attributes. Measurement 44(7), 1209–1219 (2011)

    Article  Google Scholar 

  38. Ruane, E., Farrell, S., Ventresque, A.: User perception of text-based chatbot personality. In: Følstad, A., et al. (eds.) CONVERSATIONS 2020. LNCS, vol. 12604, pp. 32–47. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68288-0_3

    Chapter  Google Scholar 

  39. Uleman, J.S., Adil Saribay, S., Gonzalez, C.M.: Spontaneous inferences, implicit impressions, and implicit theories. Annu. Rev. Psychol. 59, 329–360 (2008)

    Article  Google Scholar 

  40. Upadhyay, A.K., Khandelwal, K.: Applying artificial intelligence: implications for recruitment. Strat. HR Rev. (2018)

    Google Scholar 

  41. Valente, F., Kim, S., Motlicek, P.: Annotation and recognition of personality traits in spoken conversations from the AMI meetings corpus. In: Thirteenth Annual Conference of the International Speech Communication Association (2012)

    Google Scholar 

  42. Vinciarelli, A., Mohammadi, G.: A Survey of Personality Computing. IEEE Trans. Affect. Comput. 5(3) (2014). DOI: https://doi.org/10.1109/TAFFC.2014.2330816

  43. Walker, M., Vetter, T.: Changing the personality of a face: perceived big two and big five personality factors modeled in real photographs. J. Pers. Soc. Psychol. 110(4), 609–624 (2016). https://doi.org/10.1037/pspp0000064

    Article  Google Scholar 

  44. Yu, J., Markov, K.: Deep learning based personality recognition from facebook status updates. In: 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), pp. 383–387. IEEE, Taichung (2017). https://doi.org/10.1109/ICAwST.2017.8256484, https://ieeexplore.ieee.org/document/8256484/

  45. Yu, M., Gilmartin, E., Litman, D.: Identifying personality traits using overlap dynamics in multiparty dialogue. arXiv preprint arXiv:1909.00876 (2019)

  46. Zou, Z., Shi, Z., Guo, Y., Ye, J.: Object Detection in 20 Years: A Survey. ResearchGate (2019). www.researchgate.net/publication/333077580

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Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Peter Zhuowei Gan, Arcot Sowmya & Gelareh Mohammadi

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  1. Peter Zhuowei Gan
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  2. Arcot Sowmya
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  3. Gelareh Mohammadi
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Corresponding author

Correspondence to Peter Zhuowei Gan .

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Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Gan, P.Z., Sowmya, A., Mohammadi, G. (2022). Zero-shot Personality Perception From Facial Images. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

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