Predict the writer's trait emotional intelligence from reproduced calligraphy

Trait emotional intelligence (EI) describes an individual's ability to control their emotions. In Chinese calligraphy, there is a saying that "the character reflects the person." This raises a hypothesis: is it possible to predict a writer's trait EI from their calligraphy reproductions? To test this hypothesis, we propose a predictive method that integrates deep learning with aesthetic features of calligraphy. First, a hard pen calligraphy reproduction dataset was constructed, consisting of 48,826 reproduced characters from 191 participants, with corresponding trait EI scores and reproduction skill score ratings. A Siamese neural network was then used to extract deep feature differences between the reproduction characters and the reference characters, which were further combined with handcrafted features for regression-based predictions. Experimental results show that, using Mean Absolute Error (MAE), Mean Squared Error (MSE) and Pearson Correlation Coefficient (PCC) as evaluation metrics, this method's ability to predict the writer's trait EI from calligraphy reproductions (MAE: 0.463, MSE: 0.462, PCC: 0.730) significantly outperforms human evaluative abilities (MAE: 1.006, MSE: 1.740, PCC: 0.145), confirming that calligraphy reproductions indeed contain latent information about the writer's trait EI.