Factex: A Practical Approach to Crime Detection

The crime on roads is a major problem faced today by all the modern cities. Road Transport is the most common escape route for many criminals. Thefts and many other crimes remain unregistered and unsolved due to lack of evidence. Effective tracking of vehicles and criminals is still a big problem and involves plenty of resources. To evade such a condition, we have proposed a machine learning-based practical crime detection system using the text and face recognition techniques. Such systems will be proved useful in parking lots, toll stations, airports, border crossings, etc. In the proposed system, the text recognition involves extracting the characters present in the Indian number plates and the predicted output will be compared with the registered vehicle database. Simultaneously, Face recognition feature constitutes identifying criminal faces based on certain face regions and then mapping the respected coordinates with the criminal database. The proposed system presented in this research paper targets to deliver improvised outcomes considering the time constraints and accuracy with more than 85% successful recognitions in normal working conditions with the goal to accomplish the successful detection of crime using machine learning algorithms such as KNN, SVM, and face detection classifiers to present a practical real time detection.