Real-time interview analysis using Deep-Learning

Traditional interviews often lack objectivity and fail to provide candidates with constructive, real-time feedback, resulting in inconsistent evaluation outcomes. Moreover, human bias and subjectivity in assessment can significantly affect the fairness of recruitment and skill evaluation processes .To address these limitations, this study proposes an intelligent, adaptive system capable of analyzing both verbal and non-verbal cues during interviews using deep learning methodologies. The proposed system employs computer vision techniques for facial emotion recognition and natural language processing (NLP) for dynamic, context-aware question generation ..By capturing real-time video input, detecting emotional expressions through facial landmarks, and assessing responses using semantic and sentiment analysis, the system aims to emulate human interviewer behavior while providing objective feedback and performance analytics .This approach enhances candidate evaluation accuracy and transparency, enabling scalable applications in HR automation, mock interview simulations, and behavioral assessment platforms.