Your Face Mirrors Your Deepest Beliefs--Predicting Personality and Morals through Facial Emotion Recognition
Name
futureinternet-14-00005-v2.pdf
Size
9.45 MB
Format
Adobe PDF
Checksum (MD5)
a8a9d17db2b903daaec131bd1451127c
Author(s)
Gloor, Peter A.
Fronzetti Colladon, Andrea
Altuntas, Erkin
Cetinkaya, Cengiz
Kaiser, Maximilian F.
Ripperger, Lukas
Schaefer, Tim
Date Issued
December 22, 2021
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Future Internet 14 (1): 5 (2022)
Version
Final published version
Abstract
Can we really “read the mind in the eyes”? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual’s face through facial emotion recognition (FER) while watching a series of 15 short videos of different genres. To calibrate the system, we invited 85 people to watch the videos, while their emotional responses were analyzed through their facial expression. At the same time, these individuals also took four well-validated surveys of personality characteristics and moral values: the revised NEO FFI personality inventory, the Haidt moral foundations test, the Schwartz personal value system, and the domain-specific risk-taking scale (DOSPERT). We found that personality characteristics and moral values of an individual can be predicted through their emotional response to the videos as shown in their face, with an accuracy of up to 86% using gradient-boosted trees. We also found that different personality characteristics are better predicted by different videos, in other words, there is no single video that will provide accurate predictions for all personality characteristics, but it is the response to the mix of different videos that allows for accurate prediction.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/fi14010005
