Graphology analysis for detecting hexaco personality and character through handwriting images by using convolutional neural networks and particle swarm optimization methods

Abstract

Graphology or handwriting analysis can be used to infer the traits of the writers by examining each stroke, space, pressure, and pattern of the handwriting. In this study, we infer a six-dimensional model of human personality (HEXACO) using a Convolutional Neural Network supported by Particle Swarm Optimization. These personalities include Honesty-Humility, Emotionality, eXtraversion, Agreeableness (versus Anger), Conscientiousness, and Openness to Experience. A digital handwriting sample data of 293 different individuals associated with 36 types of personalities were collected and derived from the HEXACO space. A convolutional neural network model called GraphoNet is built and optimized using Particle Swarm Optimization (PSO). The PSO is used to optimize epoch, minibatch, and droupout parameters on the GraphoNet. Although predicting 32 personalities is quite challenging, the GraphoNet predicts personalities with 71.88% accuracy using epoch 100, minibatch 30 and dropout 52% while standard AlexNet only achieves 25%. Moreover, GraphoNet can work with lower resolution (32 x 32 pixels) compared to standard AlexNet (227 x 227 pixels).