Abstract
In this paper, artificial intelligence based study which aims to automatically detect personality traits from video by using machine learning and image processing techniques is explained. To determine the character and emotion of people by analyzing their behavior is already an important task for especially human resources. If a person's body language is adequately analyzed, much information can be obtained about that person's personality traits. The main task of this study is to predict the five factor model personality dimensions from video images by using machine learning techniques and artificial neural networks. In this study, video images and the emotional states of the person obtained from videos were utilized and an artificial intelligence based system was developed to be able to predict automatically the personality traits of a person from videos with 0, 8951 accuracy by using artificial neural networks, machine learning and image processing techniques. Within the scope of the study, prediction of 5 important personality traits, extraversion, agreeableness, conscientiousness, neuroticism and openness were emphasized.
Translated title of the contribution | Prediction of Personality Traits from Videos by Using Machine Learning Algorithms |
---|---|
Original language | Turkish |
Title of host publication | UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 778-782 |
Number of pages | 5 |
ISBN (Electronic) | 9781728139647 |
DOIs | |
Publication status | Published - Sept 2019 |
Event | 4th International Conference on Computer Science and Engineering, UBMK 2019 - Samsun, Turkey Duration: 11 Sept 2019 → 15 Sept 2019 |
Publication series
Name | UBMK 2019 - Proceedings, 4th International Conference on Computer Science and Engineering |
---|
Conference
Conference | 4th International Conference on Computer Science and Engineering, UBMK 2019 |
---|---|
Country/Territory | Turkey |
City | Samsun |
Period | 11/09/19 → 15/09/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.