Abstract:
Personality is a unique thing that everyone has and it shows how a person acts both in daily life and
at work. Therefore, tracking a person's personality has become more important, especially for an
employer. Within this context, the purpose of this research is to identify a person's personality
through big five-factor personality traits based on his/her handwritten signature. The majority of
earlier researchers have focused on analyzing handwritten signatures to describe personality with the
help of graphology. The current research was designed a way to apply graphology on the signature
image and improve the performance using neural networks. In this study, the personality of a person
was evaluated based on four selected features of a signature -namely, the size, curved start, pen
pressure, and underline. Further, an online questionnaire, which was conducted with the participation
of 500 selected individuals, has been utilized to measure and gather the personality of each person.
The complete system evaluates signature samples based on the above features and divided into four
modules. Then these four modules were fed into the feature extraction model, which analyzed the
input image with the Convolutional Neural Network (CNN) model and all four features were
extracted from the signature data set. After that, the extracted features were combined with the online
questionnaire test result to help with supervised learning. As the final output, this model predicts the
correct big five-factor personality values with 85% accuracy, when a person wrote his/her signature
on a paper. This solution is unique as this predicts the big-five factor personality traits based on the
signature for the first time and this is a more efficient approach compared to other existing work
Keywords: Convolutional Neural Network; Personality Traits; Signature Analysis; Supervised
Learning
