Psychological Counseling and Character Analysis Algorithm Based on Image Emotion

Cao, Min; Zhang, Wan

doi:10.1109/access.2020.3020236

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…This section shows the results obtained when training and testing the models presented in sections V-B and V-C. Experiments were performed coding the models in the Python programming language using the TensorFlow and Keras frameworks 1 .…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Research in personality is relevant to any computer area involving understanding or prediction of human behavior. As recent works show, there is an increasing interest on this topic [1]- [5]. Personality traits can be extracted from sources of information as diverse as speech and handwriting [6] or the use of social networks [7]- [10], but this private information is not always available, or requires consent of users; it is not possible to obtain personal diaries, monitor their social network activity, read private messages from e-mail or on-line conversations.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Experiments and Discussionmentioning

confidence: 99%