There is an abnormal increase in the crime rate, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the main hurdles for our police force since there is a large ratio gap between the number of crimes taken place and the availability of police personnel to combat the crime. With the improved security measures, the government has implemented some robust security technologies, especially CCTV cameras have been installed in many public and private areas to provide 24x7 surveillance. The footage of the CCTV can be used to identify suspects on the scene. In this project, a facial recognition system for the identification of criminals has been proposed. This system will be able to detect the face and recognize face automatically in real-time. Face detection classifiers are shared by open source communities, such as OpenCV. This project demonstrates the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer. One of the most famous face recognition algorithms, the LBPH algorithm using Haar feature-based cascade classifier will also be explained, the most important applications is in the field of face detection. Face detection is a very powerful tool for face recognition, face tracking, video surveillance, autofocusing, and human-computer interface systems. Some other application areas of face recognition are Information security, law enforcement and surveillance, smart cards, access controls.
OpenCV2 is used for providing a medium to identify frontal_face and fetch facial features using Haarcascade.
pip install opencv-python
Pillow is used to manage hundreds of criminal images in a ordered manner.
pip install Pillow
Numpy is used for converting the gived dataset to a training model and deploying the plugin in production with ease.
pip install numpy
PyMySql is used to refer to the criminal database and ping the collection everytime a criminal is detected by the model.
pip install PyMySQL
