Real-Time AI-Driven Stress Detection in Police Interrogations: Bridging Ethical Standards with Advanced Facial Recognition

Aravind H, Jogimol Joseph

Abstract: This project presents a cutting-edge, real-time facial recognition system to evaluate stress levels during police interrogations. By utilizing advances in artificial intelligence and computer vision, the system identifies subtle facial expressions and physiological indicators such as micro expressions, eye activity, and pupil changes to monitor stress responses as they occur. Its main goal is to support law enforcement with objective, data-driven insights that can help pinpoint potential suspects and optimize interrogation efficiency. Through the integration of deep learning algorithms and motion tracking techniques, the system produces accurate stress metrics, which are visually represented through an intuitive interface. Ethical standards, including data privacy and algorithmic fairness, are integral to the system?s design, ensuring equitable performance across various demographic groups. Although primarily focused on improving investigative methods, the technology also holds promise in other areas such as mental health assessment, mediation, and employee well-being monitoring. While there are ongoing challenges in refining precision and safeguarding personal data, the project aspires to transform investigative practices by merging AI-based analysis with strong ethical and scientific foundations.

Keywords: Stress detection, real time monitoring, facial expression, workplace assessment