Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Gavrilescu, Mihai; Vizireanu, Nicolae

doi:10.1186/s13640-017-0211-4

Cited by 18 publications

(9 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following study verified that facial expressions on videos implicated 442 participants and achieved accuracy at minimum 5 and 83.5% for maximum (Teijeiro-Mosquera et al, 2014). Another investigation foresees facial expressions showing emotion implicated 64 participants using 16 PF reached validity for predictive at 80% (Gavrilescu and Vizireanu, 2017). Another research carried out 186 datasets used in facial expressions reached a prediction accuracy minimum at 53.37 and maximum at 82.02% with twenty different classes involved (Zhang et al, 2017).…”

Section: Introductionsupporting

confidence: 60%

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Perdana¹,

Santosa²,

Setiawan³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

After obtaining the consistency of Visual Evoked Potential (VEP) by using a stimulus with data-entry user interface design. This study tries to confirm whether an individual's VEP will be classified according to personality type, which results from the proceeds of conventional personality tests usually carried out by the organization that is the place of this research. The results of this study will be an attempt to complement the needs of particular organizations for the elicitation stage to show user personas by using personality types as something to consider in software development in organizations. The recording of 20 participants whose personality types were known from the results of personality tests at the organization was completed. Each individual has a unique VEP. to confirm personality types and it is necessary to prove the similarity of VEP between individuals of the same type. 93.75% of VEP is classified according to personality type. Therefore it is necessary to consider that personality type can be used as user persona for user classification at the stage of user needs. To identify personality types at the personality test stage, it is still necessary to add more participants involved to get more convincing results.

show abstract

Section: Introductionsupporting

confidence: 60%

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Perdana¹,

Santosa²,

Setiawan³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…[7] investigated the physiological correlation of emotion and personality using commercial sensors and found that the emotion-to-personality relationship is better captured by non-linear rather than linear statistics. [8] proposed a threelayer neural network-based architecture for predicting the sixteen personality factors from faces analyzed using facial action coding system.…”

Section: Introductionmentioning

confidence: 99%

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Zhang

Peng

Winkler

2022

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

Personality and emotion are both central to affective computing. Existing works solve them individually. In this paper we investigate if such high-level affect traits and their relationship can be jointly learned from face images in the wild. To this end, we introduce PersEmoN, an end-to-end trainable and deep Siamese-like network which we call emotion network and personality network, respectively. It consists of two convolutional network branches, one for emotion and the other for apparent personality. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Emotion and personality networks are dedicated to their own annotated dataset. An adversarial-like loss function is further employed to promote representation coherence among heterogeneous dataset sources. Based on this, the emotion-to-personality relationship is also well explored. Extensive experiments are provided to demonstrate the effectiveness of PersEmoN.

show abstract

“…Although personality traits appear to be somewhat predictable, there can be variations in predictions for the same person and different face images, e.g., due to facial expression and pose [62,45,72,61]. For automated recognition of personality traits, two deep learning methods have been presented where accuracies between 50 % and 80 % were reported for different personality traits [76,23], measured by the 16 Personality Factors model. Also, a patent was filed recently for automatic personality and capability prediction from faces [66].…”

Section: Related Workmentioning

confidence: 99%

“…Also, predicting more subjective quantities such as facial beauty has been studied with machine learning methods [70,21]. Even more subtle attributes such as self-reported personality traits have been investigated in terms of their predictability from face images with deep learning methods [76,23]. Thus, a variety of properties have been automatically derived from face images.…”

Section: Introductionmentioning

confidence: 99%

Learning Preference-Based Similarities from Face Images using Siamese Multi-Task CNNs

Gessert,

Schlaefer

2020

Preprint

View full text Add to dashboard Cite

Online dating has become a common occurrence over the last few decades. A key challenge for online dating platforms is to determine suitable matches for their users. A lot of dating services rely on self-reported user traits and preferences for matching. At the same time, some services largely rely on user images and thus initial visual preference. Especially for the latter approach, previous research has attempted to capture users' visual preferences for automatic match recommendation. These approaches are mostly based on the assumption that physical attraction is the key factor for relationship formation and personal preferences, interests, and attitude are largely neglected. Deep learning approaches have shown that a variety of properties can be predicted from human faces to some degree, including age, health and even personality traits. Therefore, we investigate the feasibility of bridging image-based matching and matching with personal interests, preferences, and attitude. We approach the problem in a supervised manner by predicting similarity scores between two users based on images of their faces only. The ground-truth for the similarity matching scores is determined by a test that aims to capture users' preferences, interests, and attitude that are relevant for forming romantic relationships. The images are processed by a Siamese Multi-Task deep learning architecture. We find a statistically significant correlation between predicted and target similarity scores. Thus, our results indicate that learning similarities in terms of interests, preferences, and attitude from face images appears to be feasible to some degree.

show abstract

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

Cited by 18 publications

References 69 publications

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

Confirmation of Personality Types Using Visual Evoked Potential with User Interface Design Stimulus

PersEmoN: A Deep Network for Joint Analysis of Apparent Personality, Emotion and Their Relationship

Learning Preference-Based Similarities from Face Images using Siamese Multi-Task CNNs

Contact Info

Product

Resources

About