Healthcare Face Recognition System

Key Features

Security

Secure Patient Registration

Encrypted facial data storage with AES-256 encryption and HIPAA/GDPR compliance

Identification

Real-time Identification

Fast and accurate patient identification with confidence scoring and anti-spoofing protection

AI Analysis

AI Genetic Screening

Pre-screening for genetic disorders using advanced AI models and facial feature analysis

Fairness

Bias Detection

Comprehensive fairness monitoring and bias correction across all demographic groups

Compliance

Consent Management

Granular consent tracking and management for different data usage scenarios

Protection

Anti-spoofing Security

Advanced liveness detection and multi-factor authentication to prevent fraud

Installation & Setup

1

Clone the Repository

                                git clone https://github.com/omsherikar/face_recognition.git
cd face_recognition
                            

2

Run the Installation Script

                                chmod +x install.sh
./install.sh
                            

3

Start the Server

                                source venv/bin/activate
python3 main.py
                            

4

Access the API

The system will be available at http://localhost:8001

API documentation: http://localhost:8001/docs

API Documentation

Core Endpoints

POST /register-patient

Register a new patient with facial data and medical information

POST /identify-patient

Identify a patient from facial image with optional genetic screening

POST /update-consent

Update patient consent for different data usage scenarios

GET /system-statistics

Get comprehensive system statistics and monitoring data

GET /fairness-report

Generate bias detection and fairness monitoring report

Quick Start Example

                    # Register a patient
curl -X POST http://localhost:8001/register-patient \
  -H "Authorization: Bearer your_token" \
  -F "patient_id=P001" \
  -F "name=John Doe" \
  -F "date_of_birth=1990-01-15" \
  -F "medical_record_number=MRN123456" \
  -F "consent_given=true" \
  -F "image=@patient_photo.jpg"

# Identify a patient
curl -X POST http://localhost:8001/identify-patient \
  -H "Authorization: Bearer your_token" \
  -F "image=@patient_photo.jpg" \
  -F "session_id=checkin_001"
                

System Requirements

Python 3.8+

Required for running the application and all dependencies

4GB RAM

Minimum recommended memory for optimal performance

Camera Support

Webcam or camera for real-time face capture and processing

Secure Environment

HIPAA-compliant infrastructure for healthcare deployment