Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features
Abstract We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from ea...
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2017-08-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Online Access: | http://link.springer.com/article/10.1186/s13640-017-0211-4 |
| _version_ | 1811281820517400576 |
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| author | Mihai Gavrilescu Nicolae Vizireanu |
| author_facet | Mihai Gavrilescu Nicolae Vizireanu |
| author_sort | Mihai Gavrilescu |
| collection | DOAJ |
| description | Abstract We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are computed, an intermediary level where an AU activity map is built containing all AUs’ intensity levels fetched from the base layer in a frame-by-frame manner, and a top layer consisting of 16 feed-forward neural networks trained via backpropagation which analyze the patterns in the AU activity map and compute scores from 1 to 10, predicting each of the 16 personality traits. We show that the proposed architecture predicts with an accuracy of over 80%: warmth, emotional stability, liveliness, social boldness, sensitivity, vigilance, and tension. We also show there is a significant relationship between the emotions elicited to the analyzed subjects and high prediction accuracy obtained for each of the 16 personality traits as well as notable correlations between distinct sets of AUs present at high-intensity levels and increased personality trait prediction accuracy. The system converges to a stable result in no more than 1 min, making it faster and more practical than the Sixteen Personality Factors Questionnaire and suitable for real-time monitoring of people’s personality traits. |
| first_indexed | 2024-04-13T01:40:29Z |
| format | Article |
| id | doaj.art-1060bac9b46b40f3916bd0351554fca4 |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-04-13T01:40:29Z |
| publishDate | 2017-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-1060bac9b46b40f3916bd0351554fca42022-12-22T03:08:12ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812017-08-012017111910.1186/s13640-017-0211-4Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial featuresMihai Gavrilescu0Nicolae Vizireanu1Department of Telecommunications, University “Politehnica” of BucharestDepartment of Telecommunications, University “Politehnica” of BucharestAbstract We propose a novel three-layered neural network-based architecture for predicting the Sixteen Personality Factors from facial features analyzed using Facial Action Coding System. The proposed architecture is built on three layers: a base layer where the facial features are extracted from each video frame using a multi-state face model and the intensity levels of 27 Action Units (AUs) are computed, an intermediary level where an AU activity map is built containing all AUs’ intensity levels fetched from the base layer in a frame-by-frame manner, and a top layer consisting of 16 feed-forward neural networks trained via backpropagation which analyze the patterns in the AU activity map and compute scores from 1 to 10, predicting each of the 16 personality traits. We show that the proposed architecture predicts with an accuracy of over 80%: warmth, emotional stability, liveliness, social boldness, sensitivity, vigilance, and tension. We also show there is a significant relationship between the emotions elicited to the analyzed subjects and high prediction accuracy obtained for each of the 16 personality traits as well as notable correlations between distinct sets of AUs present at high-intensity levels and increased personality trait prediction accuracy. The system converges to a stable result in no more than 1 min, making it faster and more practical than the Sixteen Personality Factors Questionnaire and suitable for real-time monitoring of people’s personality traits.http://link.springer.com/article/10.1186/s13640-017-0211-4 |
| spellingShingle | Mihai Gavrilescu Nicolae Vizireanu Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features EURASIP Journal on Image and Video Processing |
| title | Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features |
| title_full | Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features |
| title_fullStr | Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features |
| title_full_unstemmed | Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features |
| title_short | Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features |
| title_sort | predicting the sixteen personality factors 16pf of an individual by analyzing facial features |
| url | http://link.springer.com/article/10.1186/s13640-017-0211-4 |
| work_keys_str_mv | AT mihaigavrilescu predictingthesixteenpersonalityfactors16pfofanindividualbyanalyzingfacialfeatures AT nicolaevizireanu predictingthesixteenpersonalityfactors16pfofanindividualbyanalyzingfacialfeatures |