A Comparative Study on Facial Detection for Criminal Probe

The potential of crime and violation is intended to be controlled. Computationally, accurate crime predictions and future trend analysis can help to improve metropolis safety. We are all aware that a person's face is an exceptional and significant component of their physical nature. As a result, we can use it to track down a criminal's identification. As a result of technological advancements, CCTV is now installed in many public locations to record illegal activity. The criminal face recognition system can be used with the previously photographed faces and criminals' photos that are on hand in the police station. The primary objective of this study is to determine how law enforcement authorities may use a combination of ML and computer vision to detect, prevent, and solve crimes considerably more rapidly and correctly. The precise estimation of the crime rate, types, and hotspots using historical trends presents numerous computational opportunities and challenges. Despite significant research efforts, a better predictive algorithm is still required to lead police patrols in the direction of criminal activity. The linear regression, support vector machine (SVM), Naive Bayes, k-nearest neighbours (KNN), decision tree, random forest, K-means clustering and neural networks are some of the machine learning techniques used in this system. Our proposed system will detect crime as well as the criminal hotspots on which crime is likely to take place using hidden patterns from criminal datasets.