Document

Table of Contents

Introduction
System Overview
Architecture Design
Component Design

1.Introduction

1.1 Purpose
This document provides a high-level design for a Multi-Tenant Face Recognition System that enables

businesses to integrate face recognition capabilities into their operations through a P2P (Peer-to-Peer) architecture. The system allows multiple businesses to register, manage users, configure cameras, and receive real-time recognition events.
1.2 Scope
The system covers: Multi-tenant business management User registration with multiple face images Camera configuration and monitoring Real-time face recognition and event processing Unknown person detection and tracking Access log management API-based integration with external systems Webhook-based event notifications Security and authentication mechanisms
1.3 Definitions and Acronyms
Term Definition P2P Peer-to-Peer - Decentralized architecture where businesses interact directly API Application Programming Interface RTSP Real-Time Streaming Protocol RTMP Real-Time Messaging Protocol UUID Universally Unique Identifier HMAC Hash-based Message Authentication Code ML Machine Learning RPS Requests Per Second SLA Service Level Agreement

1.4 References
Original System Diagram Document (Face Recognition.docx) Database ERD and Schema Industry standards for face recognition (NIST FRVT) GDPR and data privacy regulations

2. System Overview

2.1 Business Context

The system provides face recognition as a service to multiple businesses, allowing them to: Register employees/users with facial biometric data Monitor access through camera-enabled locations Receive real-time alerts on recognition events Track unknown persons for security Generate access reports and analytics
2.2 Key Stakeholders
Stakeholder Role Interests Business Administrators Configure system, manage users Easy integration, reliable service Security Personnel Monitor unknown persons Real-time alerts, accurate detection Employees/Users Subjects of recognition Privacy, accuracy System Administrators Maintain infrastructure Stability, performance API Consumers External system integration API reliability, documentation

2.3 High-Level Requirements
Functional Requirements
FR-001: Support multiple independent businesses (multi-tenancy)
FR-002: Allow users to register with 3-5 face images
FR-003: Support IP camera integration via RTSP/RTMP
FR-004: Perform real-time face detection and recognition
FR-005: Track and store unknown persons
FR-006: Generate access logs with timestamps
FR-007: Provide REST API for external integration
FR-008: Send webhooks for real-time event notifications
FR-009: Support role-based access control
FR-010: Provide camera status monitoring
Non-Functional Requirements
NFR-001: Process recognition events within 500ms
NFR-002: Support 99.5% uptime SLA
NFR-003: Handle 1000+ cameras per business
NFR-004: Scale to 100+ concurrent businesses
NFR-005: Maintain recognition accuracy > 95%
NFR-006: Store data with encryption at rest

NFR-007: Provide audit trails for all operations

3. Architecture Design

3.1 Architecture Style
Microservices Architecture with event-driven components
3.2 Architectural Patterns
Multi-Tenant SaaS: Isolated data per business Event-Driven Architecture: Asynchronous event processing API Gateway Pattern: Single entry point for external requests CQRS Pattern: Separate read/write operations for performance Circuit Breaker: Fault tolerance for external integrations

4. Component Design

4.1 API Gateway
Responsibilities:
Authentication and authorization Request routing to appropriate services Rate limiting and throttling API versioning Request/response transformation SSL/TLS termination API analytics and monitoring Technology: Kong / AWS API Gateway / Azure API Management Key Features: JWT token validation API key authentication

Request signature verification Circuit breaker for downstream services Request logging
4.2 Business Management Service
Responsibilities:
Business registration and onboarding Business profile management Business configuration settings Business-level access control Subscription and billing management Endpoints:

Data Entities: Businesses Business Settings API Users
4.3 User Management Service
Responsibilities:
User registration and profile management
Face image upload and storage
Face encoding generation
User search and filtering
User status management
Bulk user import/export
Endpoints:
POST /api/v1/businesses
GET /api/v1/businesses/{business_id}
PUT /api/v1/businesses/{business_id}
DELETE /api/v1/businesses/{business_id}
GET /api/v1/businesses/{business_id}/settings
PUT /api/v1/businesses/{business_id}/settings

Data Entities:
Registered Users
User Face Images
Processing Flow:

Receive face image upload
Validate image quality
Extract face from image
Generate face encoding using ML model
Calculate face landmarks
Store image in object storage
Save metadata and encoding in database

4.4 Camera Management Service
Responsibilities:
Camera registration and configuration

Camera location management
Camera status monitoring
Stream URL management
Camera health checks
Camera settings configuration
Endpoints:
POST /api/v1/businesses/{business_id}/users
GET /api/v1/businesses/{business_id}/users
GET /api/v1/businesses/{business_id}/users/{user_id}
PUT /api/v1/businesses/{business_id}/users/{user_id}
DELETE /api/v1/businesses/{business_id}/users/{user_id}
POST /api/v1/businesses/{business_id}/users/{user_id}/faces
GET /api/v1/businesses/{business_id}/users/{user_id}/faces
DELETE /api/v1/businesses/{business_id}/users/{user_id}/faces/{face_id}

Data Entities:
Cameras
Camera Locations
4.5 Camera Streamer Service
Responsibilities:
Connect to IP cameras via RTSP/RTMP
Normalize video streams
Extract frames at configurable intervals
Preprocess frames for face detection
Push frames to message queue
Handle stream reconnection
Monitor stream health
Technology Stack:
FFmpeg for stream processing
OpenCV for frame extraction
GStreamer (alternative)
Configuration:
Frame extraction rate: 2-5 FPS
Image resolution: 640x480 or higher
Supported protocols: RTSP, RTMP, HTTP
Reconnection strategy: Exponential backoff
Processing Flow:

4.6 Face Recognition Engine (Core AI Service)
POST /api/v1/businesses/{business_id}/cameras
GET /api/v1/businesses/{business_id}/cameras
GET /api/v1/businesses/{business_id}/cameras/{camera_id}
PUT /api/v1/businesses/{business_id}/cameras/{camera_id}
DELETE /api/v1/businesses/{business_id}/cameras/{camera_id}
POST /api/v1/businesses/{business_id}/cameras/{camera_id}/heartbeat
GET /api/v1/businesses/{business_id}/cameras/{camera_id}/status

Camera Stream → Stream Reader → Frame Extractor →
Image Preprocessor → Message Queue (frame_ready)

Responsibilities:
Face detection in frames
Face recognition and matching
Confidence score calculation
Face encoding generation
Match user against database
Generate recognition events

Handle multiple faces in frame
Unknown person detection
Technology Stack:
Face Detection: MTCNN / Haar Cascade / YOLO
Face Recognition: FaceNet / DeepFace / ArcFace
Framework: TensorFlow / PyTorch
Face Encoding: 128/512-dimensional vectors
Processing Pipeline:

Matching Algorithm:
Input Frame → Face Detection → Face Alignment →
Feature Extraction → Encoding Generation →
Database Matching → Event Generation

python

Performance Optimization:
Use Redis cache for frequently accessed face encodings
Batch processing for multiple faces
GPU acceleration for encoding generation
Horizontal scaling with load balancing
4.7 Webhook Dispatcher Service
Responsibilities:
Send recognition events to business webhooks
Retry failed deliveries
Sign webhook payloads
Track delivery status
Handle timeout scenarios
Queue management Delivery Strategy: def match_face(face_encoding, business_id):
json:

{
"event_id": "uuid",
"event_type": "RECOGNIZED",
"business_id": "uuid",
"timestamp": "2025-10-09T10:30:00Z",
"camera": {
"camera_id": "uuid",
"camera_name": "Entrance Camera 01",
"location": "Main Entrance"
},
"user": {
"user_id": "uuid",
"name": "John Doe",
"employee_id": "EMP001"
},
"recognition": {
"confidence_score": 0.94,
"image_url": "https://storage.example.com/events/12345.jpg"
}

API Response Format

Success Response:
json:

{
"success": true,
"data": {
"user_id": "uuid",
"first_name": "John",
"last_name": "Doe",
"status": "ACTIVE"
},
"message": "User created successfully",
"timestamp": "2025-10-09T10:30:00Z"
}

Error Response:

{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid input data",
"details": [
{
"field": "email",
"message": "Invalid email format"
}
]
},
"timestamp": "2025-10-09T10:30:00Z"
}

Pagination Response:

{
"success": true,
"data": [...],
"pagination": {
"page": 1,
"limit": 50,
"total_records": 150,
"total_pages": 3
},
"timestamp": "2025-10-09T10:30:00Z"
}