Face Detection Token System for Hospitals Utilizing Python

Project Synopsis & Overview

The proposed Face Detection Token System for Hospital is a Python-based computer vision system designed to automate patient identification, token generation, and attendance tracking in hospital environments. The system addresses limitations of traditional hospital check-in procedures, including manual processing, long waiting times, incorrect identification, and lack of automation. Its main objective is to streamline patient registration and attendance management by using facial recognition to identify registered patients and generate unique tokens automatically. The system captures patient images through a webcam and uses OpenCV for face detection and recognition. The PPT identifies Haar Cascade Classifiers for face detection and LBPH (Local Binary Pattern Histogram) for face recognition and training. The Patient Registration Module captures patient details such as name and ID along with facial images, which are used for subsequent recognition. After successful identification, the Token Generation Module generates a unique patient token, while the Attendance Logging Module records the token, patient name, date, and time in a CSV file. The Report Generation and Saving Module creates timestamped CSV reports for hospital staff. The system is intended to reduce patient waiting time, improve identification accuracy and security, simplify hospital attendance management, and support efficient patient-flow administration.

Complete Technical Specifications