Multimodal Intelligence in Recruitment: Modelling Personality and Behavioural Traits from Video Interviews

With advances in AI and deep learning, automated personality analysis from video interviews has emerged as a key area in personality computing and psychological assessment. Leveraging computer vision and pattern recognition, modern models now interpret nonverbal cues to estimate personality traits directly from visual input. The recruitment landscape often depends on manual assessments that are susceptible to bias and inconsistency, making objective candidate evaluation challenging. While asynchronous video interviews (AVIs) offer scalability and convenience, they still fall short in capturing deeper personality-related cues. This research introduces an Automatic Personality Recognition (APR) framework that leverages multimodal data—text, audio, and visuals—to assess candidates along the Big Five personality traits. By applying advanced deep learning techniques to analyze recorded interviews, the system delivers objective and scalable personality evaluations. This approach enhances the fairness and effectiveness of hiring decisions, addressing key limitations in both conventional and technology-driven recruitment practices.