Deciphering Crime Suspects' Emotion for Investigative Precision using Deep Learning

Abstract

Using Convolutional Neural Networks (CNNs) for Facial Emotion Recognition (FER) in criminal investigations is the main goal of the proposed study. Using cutting-edge CNN architectures trained on a variety of datasets to precisely identify emotions including joy, anger, fear, surprise, disgust, and sadness is one of the main components. The research seeks to improve the effectiveness of investigations by giving law enforcement a tool to interpret the subtle emotional cues in the facial expressions of potential suspects in crimes. The technique has potential uses in human-computer interface, security, and surveillance in addition to criminal investigations. The larger significance is in helping to create more precise and effective investigative procedures, which will ultimately promote a society that is safer and more secure. The study focuses on providing a brief overview, with a suggested method utilizing CNNs to trace crime suspects.