Recognition of extraversion level based on handwriting and support vector machines

This study investigated whether it is possible to train a machine to discriminate levels of extraversion based on handwriting variables. Support vector machines (SVMs) were used as a learning algorithm. Handwriting of 883 people (404 men, 479 women) was examined. Extraversion was measured using the Polish version of the NEO-Five Factor Inventory. The handwriting samples were described by 48 variables. The support vector machines were separately trained and tested for each sex, using 10-fold cross-validation. Good recognition accuracy (around .7) was achieved for 10 handwriting variables, different for men and women. The results suggest the existence of a relationship between handwriting elements and extraversion.