Criminal Detection System using Python

Criminal Detection System using Python Criminal Detection System using Python criminal face detection system project in python criminal detection using face-recognition github criminal face identification system project with source code criminal face-detection system project in java github criminal face identification system project report crime detection github crime detection using machine learning github
real-time crime detection github criminal detection system using python github criminal detection system using python pdf criminal detection using face recognition github criminal face detection system project in java github Roblem In this modern world, India’s security is becoming more and more important. Numerous organised crime operations are anticipated, with the goal of potentially undermining our defence and law enforcement forces. In this kind of situation, technology becomes crucial. Video analytics, which contains several components, is one facet of technology application in law enforcement. Finding patterns in several videos is one of the elements.

This work involves analysing a 30- to 1-minute film, taking pictures of the faces of the people in it, and then analysing another video to determine how many of the people in the first video appear again and when. Many essential elements are usually included in a Python criminal detection system, which makes use of libraries such as OpenCV, TensorFlow, and Scikit-learn. The general structure of such a system is broken down as follows:

Face Recognition:
Local Binary Patterns Histograms (LBPH):
This algorithm is commonly used for facial recognition due to its efficiency and ability to handle variations in lighting.
Deep Learning Models:
CNNs can be trained to extract features from faces, enabling high-accuracy recognition by comparing extracted features against a database.

Face Recognition:
Classifiers based on Haar Cascade: These are frequently employed because of how quickly and effectively they can identify faces in pictures or video frames.
Deep Learning Models: For more reliable and accurate face detection, particularly under difficult circumstances, Convolutional Neural Networks (CNNs) can also be used.