Criminal Detection and Reporting System
This facial recognition system captures real-time images, detects faces, and matches them to a database. Features: suspect real-time detection, PDF reports, email alerts, and location tracking.
Created on 9th June 2024
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Criminal Detection and Reporting System
This facial recognition system captures real-time images, detects faces, and matches them to a database. Features: suspect real-time detection, PDF reports, email alerts, and location tracking.
The problem Criminal Detection and Reporting System solves
Facial Recognition Suspect Detection System
This project is a facial recognition system designed for suspect detection and alerting. It uses a webcam to capture real-time images, processes them to detect faces, and compares them against a database of known suspects to enhance security and law enforcement operations.
Key Features
Face Capture and Storage
Users can add new suspects by capturing images through the webcam. The system stores their facial encodings and details in JSON files, ensuring an up-to-date database.
Face Recognition
The system continuously processes video frames to detect and recognize faces in real-time, comparing them against stored encodings of known suspects with high accuracy.
Alert System
Upon recognizing a suspect, the system generates a PDF report with the suspect's information and sends an email notification with the report attached to designated authorities for immediate action.
Additional Functionalities
Location Tracking: Integrates location data for context.
PDF Creation: Uses ReportLab for professional reports.
Email Notification: Uses SMTP to send alerts with attached reports.
Core Technologies
OpenCV: Image processing and video frame capture.
face_recognition: High-accuracy facial recognition.
NumPy: Numerical operations for facial encoding processing.
ReportLab: PDF report generation.
Geocoder: Location tracking.
smtplib and email: Sending email notifications with reports.
Challenges we ran into
One challenge we encountered was optimizing the performance of the facial recognition algorithm to ensure real-time processing of video frames. Achieving high accuracy while minimizing computational resources was a balancing act that required extensive experimentation and tuning.
Another challenge was integrating the various components of the system, such as the facial recognition module, PDF report generation, email notification system, and location tracking. Ensuring seamless communication and synchronization between these modules posed logistical and technical hurdles.
Additionally, managing large datasets of facial encodings and suspect information presented challenges related to storage, retrieval, and efficient querying. Balancing the need for a comprehensive database with performance considerations was a significant challenge.
Finally, ensuring the security and privacy of sensitive data, such as facial images and personal information, was a paramount concern. Implementing robust security measures to safeguard against unauthorized access and data breaches required careful planning and implementation.
Tracks Applied (2)
Polygon Track
Polygon
Ethereum Track
ETHIndia