# Document
**Table of Contents**
1. Introduction
2. System Overview
3. Architecture Design
4. Component Design

### 1.Introduction
**1.1 Purpose**<br>
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.<br>
**1.2 Scope**<br>
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<br>
**1.3 Definitions and Acronyms**<br>
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<br>

**1.4 References**<br>
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<br>
**2.2 Key Stakeholders**<br>
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<br>

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

NFR-007: Provide audit trails for all operations<br>

#### 3. Architecture Design<br>
**3.1 Architecture Style**<br>
Microservices Architecture with event-driven components<br>
**3.2 Architectural Patterns**<br>
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<br>
### 4. Component Design
**4.1 API Gateway**<br>
**Responsibilities:**<br>
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<br>
**4.2 Business Management Service**<br>
**Responsibilities:**<br>
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<br>
**4.3 User Management Service**<br>
**Responsibilities:**<br>
User registration and profile management<br>
Face image upload and storage<br>
Face encoding generation<br>
User search and filtering<br>
User status management<br>
Bulk user import/export<br>
Endpoints:<br>
POST /api/v1/businesses<br>
GET /api/v1/businesses/{business_id}<br>
PUT /api/v1/businesses/{business_id}<br>
DELETE /api/v1/businesses/{business_id}<br>
GET /api/v1/businesses/{business_id}/settings<br>
PUT /api/v1/businesses/{business_id}/settings<br>

**Data Entities:**<br>
Registered Users<br>
User Face Images<br>
Processing Flow:<br>
1. Receive face image upload<br>
2. Validate image quality<br>
3. Extract face from image<br>
4. Generate face encoding using ML model<br>
5. Calculate face landmarks<br>
6. Store image in object storage<br>
7. Save metadata and encoding in database<br>
**4.4 Camera Management Service**<br>
**Responsibilities:**<br>
Camera registration and configuration<br><br>
Camera location management<br>
Camera status monitoring<br>
Stream URL management<br>
Camera health checks<br>
Camera settings configuration<br>
Endpoints:<br>
POST /api/v1/businesses/{business_id}/users<br>
GET /api/v1/businesses/{business_id}/users<br>
GET /api/v1/businesses/{business_id}/users/{user_id}<br>
PUT /api/v1/businesses/{business_id}/users/{user_id}<br>
DELETE /api/v1/businesses/{business_id}/users/{user_id}<br>
POST /api/v1/businesses/{business_id}/users/{user_id}/faces<br>
GET /api/v1/businesses/{business_id}/users/{user_id}/faces<br>
DELETE /api/v1/businesses/{business_id}/users/{user_id}/faces/{face_id}<br>

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

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

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

**Responsibilities:**<br>
Face detection in frames<br>
Face recognition and matching<br>
Confidence score calculation<br>
Face encoding generation<br>
Match user against database<br>
Generate recognition events<br><br>
Handle multiple faces in frame<br>
Unknown person detection<br>
Technology Stack:<br>
Face Detection: MTCNN / Haar Cascade / YOLO<br>
Face Recognition: FaceNet / DeepFace / ArcFace<br>
Framework: TensorFlow / PyTorch<br>
Face Encoding: 128/512-dimensional vectors<br>
Processing Pipeline:<br>

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

python<br>

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

**Success Response:**<br>
**json:**
```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:**<br>

```json
{
"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:**<br>

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