AI-based Behavioural Analyser for Interviews/Viva

Abstract

Globalization and technology have made virtual interviews to be the choice of recruitment. Even though online interviews/viva have eliminated time, budgetary, and geographical barriers, the lack of comprehension regarding the interviewee’s behavioural aspects is yet to overcome. Therefore, a machine-based approach is proposed in this research for detecting and assessing changes in interviewees’ behaviour and personality traits based on nonverbal cues. Additionally, a group analysis of other applicants, as well as a comparison of the interview environment with the non-interview environment is also being obtained. To achieve this, we focus on the candidate’s emotion, eye movement, smile, and head movements. The system was carried out using deep learning and machine learning models which achieved accuracies over 85% for all smile, eye gaze, emotion, and head pose analysis. Furthermore, several machine learning models were developed based on the analysed behavioural outcomes of the interviewee to identify big five personality traits with Random Forest model yielding highest accuracy rate of over 75%. Our findings indicate that nonverbal behavioural cues can be utilized to determine personality traits.