Estimation of Personality Traits From Portrait Pictures Using the Five-Factor Model

Abstract

This work presents a model based on Deep Neural Networks for the prediction of apparent personality. It can quantify personality traits with the Five-Factor model (Big Five) from a Portrait image. In order to evaluate the effectiveness of this approach, a new corpus of 30,935 portraits with their associated personality trait was extracted from an existing resource of videos (First Impressions, ChaLearn) tagged with redundant pairwise comparisons to ensure consistency. We propose several models using Convolutional Neural Networks to automatically extract features from a portrait that are indicators of personality traits; then the models classify these characteristics into a binary class for each Big Five factor: openness to experience (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). In addition, we experiment with feature encoding and transfer learning to enrich the representation of images with additional untagged portraits (∼45,000 and ∼200M), reaching a percentage of accuracy within the state of the art (albeit not directly comparable), obtaining 65.86% as a classifier averaging the 5 factors (O=61.48%, C=69.56%, E=73.23%, A=60.68%, N=64.35%). Compared to human judgment (mean accuracy of 56.66%), the model obtained higher average performance and higher accuracy in 4 of the 5 factors of the Big Five model. In addition, in comparison with the state of the art this model shows several advantages: (1) it requires only a single portrait to make the prediction, being this a non-invasive and easily accessible resource (e.g. selfies) (2) the extraction of features from the portrait is done automatically, (3) a single model performs the extraction of characteristics and classification.

Personality of Public Health Organizations’ Instagram Accounts and According Differences in Photos at Content and Pixel Levels

“…Here, online AI services can be useful; the personality of an author can be estimated based on the text that he or she wrote. Previous studies reported that personality can be judged with equal or even more accuracy by analyzing web-logs using computers than by human observers [ 22 , 23 , 55 , 56 ], and many studies have judged the personality of authors of various texts using online AI services [ 12 , 24 , 25 , 27 , 28 ]. In the line of this research, the present study measured the personality of organizational Instagram accounts using an online AI service and investigated how their personality is related to the features of their photos at content and pixel levels.…”

Deep-Learning-Based Adaptive Advertising with Augmented Reality

“…For this step, a model previously built and developed from facial analysis in images will be taken, since these provide better results than all types of existing multimedia files (audio, text, video, and images) [ 27 ], being the model of classification: CNN-4 of the work in [ 24 , 29 ] the one selected to be integrated into the system, because despite the fact that there is a better model obtained in the same work called FaceNet-1 [ 56 ], its weight, computational requirement, and convergence time is very high, causing the selected hardware devices not to be able to support it, taking then the second best model of the work, whose precision obtained does not differ significantly from this, and in return offers better performance and speed. In Table 4 and Table 5 , a comparison of the precision in the detection of personality by Big Five is made between the various models of [ 56 ] from the images of faces, which have the following characteristics: They are in a selfie format, they are in grayscale, normalized, and their resolution is 208 × 208.…”

“…The convolutional neural network architecture of the CNN-4 classification model [ 56 ] for the detection of Big Five in the system is shown in Figure 9 .…”

“…Table 4. Comparison of the best results obtained from the different Big Five models developed in [56].…”

“…Classiffier: FaceNet-1 65.86% Table 5. Comparison between the results (each one is the percentage of precision of the detection of each personality) by dimension of personality among the best Big Five models in [56]. The convolutional neural network architecture of the CNN-4 classification model [56] for the detection of Big Five in the system is shown in Figure 9.…”