A Review of Personality Trait Recognition with Deep Learning Techniques

Abstract

Deep learning (DL) has proven to be highly successful in various classification tasks using extracted data from images, text, or sound. DL also enables computers to understand and interpret massive amounts of data, leading to applications such as self-driving cars, medical imaging analysis, and video surveillance. The maturity of deep learning techniques has also helped solve tasks in the field of affective computing, allowing computers to understand, interpret, and respond to human emotions. This has paved the way for the development of automated personality recognition, in which computers can recognize human personality traits using video analysis. A variety of deep learning techniques have been developed to learn and extract meaningful patterns and representations from audio-visual data for automatic personality trait recognition (PTR). This study aimed to explore deep learning techniques that have been used and modified in previous studies for PTR. Initially, this paper presents detailed explanations of the general process of personality trait recognition and the data modalities used. Next, this paper discusses the latest deep learning techniques that have been modified to solve personality recognition tasks. Based on the review of previous studies, the development of a PTR model using deep learning techniques combined with audio-visual modalities yielded impressive prediction results.