Personality Recognition

Personality recognition, aiming to automatically infer personality traits from various data sources like text, speech, and facial expressions, seeks to improve the accuracy and interpretability of personality assessments. Current research emphasizes multimodal approaches, leveraging combinations of audio-visual data and natural language processing techniques, often employing deep learning models such as Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and transformers, sometimes enhanced by ensemble methods like AdaBoost. This field holds significant potential for advancing human-computer interaction, personalized services (e.g., mental health support), and a deeper understanding of human behavior, particularly through the development of robust and explainable models.