Predicting the big five personality traits from hand-written text features through semi-supervised learning

Handwriting analysis is a systematic study of preserved graphic structures. Which are generated in the human brain and produced on paper in cursive or printed style. The style in which a text is written reflects an array of meta-information. Personality is a combination of an individual’s behavior, emotion, motivation, and thought-pattern characteristics. It has an impact on one’s life choices, well-being, health, and numerous other preferences. This study investigates the correlation between handwriting features and personality characteristics. The prediction of personality through handwriting analysis needs to investigate the style and structure of writing. This study extracts eleven features from handwriting samples using a graph-based writing representation approach. The Big Five model of personality traits is utilized to find the personality of the writer. To improve classification accuracy utilizes a Semi-supervised Generative Adversarial Network (SGAN). This network uses a small amount of labeled data and a larger amount of unlabeled data to train the classifier. The discriminator works as a multi-class classifier and is trained on labeled, unlabeled, and generator created data. The proposed system predicts 91.3% correct personality results by utilizing the writing features of 173 participants.