Model for Automatic Detection of the Big Five Personality Traits Through Facial Images

Giordano Barbieri Lizama, Hugo D. Calderón-Vilca

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper presents a model based on deep learning for the automatic prediction of the big five personality traits: Extraversion, Agreeableness, Responsibility, Emotional Stability and Openness to Experience. For the development of the model a set of five neural networks was used, three of them built from an Inception Resnet V2 network, a network already trained to detect the facial area of a portrait provided by the DeepFace library and a FeedForward network built in this study using the results of the previous ones. To train these networks, a dataset of 13026 facial images was generated from a set of videos provided by Chalearn [1]. The model achieved an interesting accuracy rate, 64% was obtained by averaging the 5 factors (Extraversion = 64%, Agreeableness = 72%, Responsibility = 61%, Emotional Stability = 63% and Openness to Experience = 62%). Compared to other studies, the model presented in this article has several advantages: (1) it generated a new dataset for the study (2) it supports facial images with low quality, non-static and background and (3) the trained model can work with a large pixel size (299,299,3)

Original languageEnglish
Pages (from-to)60-67
Number of pages8
JournalInternational Journal of Computer Information Systems and Industrial Management Applications
StatePublished - 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© MIR Labs


  • Affective computing
  • Big five model
  • Deep learning
  • Machine learning
  • Personality prediction
  • Personality traits


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