Personality Trait Prediction Based on 2.5D Face Feature Model

The assessment of individual personality traits plays a crucial role in important social events such as interpersonal relationships, job search, crime fighters, and disease treatment. In this paper, a multi-view (frontal and profile view, 2.5D) facial feature extraction model is proposed to evaluate the possible correlation between personality traits and face images. Our main contribution and innovation are threefold: Our primary contribution is the development of a 2.5D hybrid personality computational model in order to gain a more comprehensive understanding of one’s personality traits; the second is that we have established two datasets. Datasets of people over 35 years of age are compared with those of 16–35-year-olds. We focus on the relationship between personality traits in human face and age; Finally, on a dataset of 500 facial images pre-processed from our face database and an personality score dataset collected from human testers, we evaluate the model through the application of support vector regression (SVR). Result shows that the prediction performance of the 2.5D feature model is better than that of the 2D model. We show that the 2.5D model performs well with low statistic error (MSE = 0.4991) and good predictability (R2 = 0.5638).