Feasible Criminal Identification using Image Recognition

Abstract

Criminal identification using image recognition is a crucial task for law enforcement agencies. It involves identifying and tracking down criminals by analysing their facial features from images and videos. The system aims to improve the efficiency and accuracy of criminal identification while reducing human error. The system will be trained on large datasets of criminal images to ensure high accuracy and reliability. The paper will use publicly available datasets such as the Face Recognition Technology (FERET) and the Labelled Faces in the Wild (LFW) dataset to train and test the model. Metrics including accuracy, precision, recall, and F1-score will be used to assess the system’s efficacy. The results and discussions will focus on the strengths and weaknesses of the model, and potential avenues for improvement. In conclusion, the proposed face recognition system shows promising results in identifying criminals and reducing the burden on law enforcement agencies. The manuscript discusses the objective of the paper, provides an overview of facial recognition technology, and summarizes existing approaches in criminal identification using image recognition.