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# Document
01b9a4 Kirti 2025-10-10 12:32:54 2
**Table of Contents**
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1. Introduction
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2. System Overview
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3. Architecture Design
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4. Component Design
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### 1.Introduction
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**1.1 Purpose**<br>
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This document provides a high-level design for a Multi-Tenant Face Recognition System that enables
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businesses to integrate face recognition capabilities into their operations through a P2P (Peer-to-Peer)
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architecture. The system allows multiple businesses to register, manage users, configure cameras, and receive
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real-time recognition events.<br>
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**1.2 Scope**<br>
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The system covers:
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Multi-tenant business management
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User registration with multiple face images
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Camera configuration and monitoring
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Real-time face recognition and event processing
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Unknown person detection and tracking
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Access log management
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API-based integration with external systems
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Webhook-based event notifications
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Security and authentication mechanisms<br>
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**1.3 Definitions and Acronyms**<br>
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Term Definition
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P2P Peer-to-Peer - Decentralized architecture where businesses interact directly
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API Application Programming Interface
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RTSP Real-Time Streaming Protocol
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RTMP Real-Time Messaging Protocol
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UUID Universally Unique Identifier
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HMAC Hash-based Message Authentication Code
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ML Machine Learning
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RPS Requests Per Second
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SLA Service Level Agreement<br>
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**1.4 References**<br>
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Original System Diagram Document (Face Recognition.docx)
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Database ERD and Schema
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Industry standards for face recognition (NIST FRVT)
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GDPR and data privacy regulations
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#### 2. System Overview
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**2.1 Business Context**
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The system provides face recognition as a service to multiple businesses, allowing them to:
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Register employees/users with facial biometric data
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Monitor access through camera-enabled locations
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Receive real-time alerts on recognition events
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Track unknown persons for security
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Generate access reports and analytics<br>
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**2.2 Key Stakeholders**<br>
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Stakeholder Role Interests
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Business Administrators Configure system, manage users Easy integration, reliable service
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Security Personnel Monitor unknown persons Real-time alerts, accurate detection
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Employees/Users Subjects of recognition Privacy, accuracy
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System Administrators Maintain infrastructure Stability, performance
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API Consumers External system integration API reliability, documentation<br>
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**2.3 High-Level Requirements**<br>
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Functional Requirements<br>
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FR-001: Support multiple independent businesses (multi-tenancy)<br>
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FR-002: Allow users to register with 3-5 face images<br>
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FR-003: Support IP camera integration via RTSP/RTMP<br>
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FR-004: Perform real-time face detection and recognition<br>
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FR-005: Track and store unknown persons<br>
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FR-006: Generate access logs with timestamps<br>
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FR-007: Provide REST API for external integration<br>
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FR-008: Send webhooks for real-time event notifications<br>
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FR-009: Support role-based access control<br>
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FR-010: Provide camera status monitoring<br>
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Non-Functional Requirements<br>
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NFR-001: Process recognition events within 500ms<br>
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NFR-002: Support 99.5% uptime SLA<br>
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NFR-003: Handle 1000+ cameras per business<br>
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NFR-004: Scale to 100+ concurrent businesses<br>
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NFR-005: Maintain recognition accuracy > 95%<br>
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NFR-006: Store data with encryption at rest<br>
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NFR-007: Provide audit trails for all operations<br>
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#### 3. Architecture Design<br>
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**3.1 Architecture Style**<br>
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Microservices Architecture with event-driven components<br>
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**3.2 Architectural Patterns**<br>
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Multi-Tenant SaaS: Isolated data per business
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Event-Driven Architecture: Asynchronous event processing
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API Gateway Pattern: Single entry point for external requests
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CQRS Pattern: Separate read/write operations for performance
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Circuit Breaker: Fault tolerance for external integrations<br>
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### 4. Component Design
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**4.1 API Gateway**<br>
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**Responsibilities:**<br>
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Authentication and authorization
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Request routing to appropriate services
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Rate limiting and throttling
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API versioning
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Request/response transformation
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SSL/TLS termination
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API analytics and monitoring
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Technology: Kong / AWS API Gateway / Azure API Management
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Key Features:
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JWT token validation
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API key authentication
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Request signature verification
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Circuit breaker for downstream services
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Request logging<br>
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**4.2 Business Management Service**<br>
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**Responsibilities:**<br>
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Business registration and onboarding
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Business profile management
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Business configuration settings
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Business-level access control
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Subscription and billing management
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Endpoints:
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Data Entities:
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Businesses
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Business Settings
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API Users<br>
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**4.3 User Management Service**<br>
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**Responsibilities:**<br>
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User registration and profile management<br>
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Face image upload and storage<br>
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Face encoding generation<br>
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User search and filtering<br>
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User status management<br>
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Bulk user import/export<br>
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Endpoints:<br>
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POST /api/v1/businesses<br>
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GET /api/v1/businesses/{business_id}<br>
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PUT /api/v1/businesses/{business_id}<br>
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DELETE /api/v1/businesses/{business_id}<br>
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GET /api/v1/businesses/{business_id}/settings<br>
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PUT /api/v1/businesses/{business_id}/settings<br>
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**Data Entities:**<br>
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Registered Users<br>
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User Face Images<br>
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Processing Flow:<br>
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1. Receive face image upload<br>
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2. Validate image quality<br>
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3. Extract face from image<br>
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4. Generate face encoding using ML model<br>
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5. Calculate face landmarks<br>
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6. Store image in object storage<br>
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7. Save metadata and encoding in database<br>
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**4.4 Camera Management Service**<br>
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**Responsibilities:**<br>
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Camera registration and configuration<br><br>
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Camera location management<br>
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Camera status monitoring<br>
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Stream URL management<br>
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Camera health checks<br>
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Camera settings configuration<br>
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Endpoints:<br>
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POST /api/v1/businesses/{business_id}/users<br>
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GET /api/v1/businesses/{business_id}/users<br>
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GET /api/v1/businesses/{business_id}/users/{user_id}<br>
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PUT /api/v1/businesses/{business_id}/users/{user_id}<br>
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DELETE /api/v1/businesses/{business_id}/users/{user_id}<br>
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POST /api/v1/businesses/{business_id}/users/{user_id}/faces<br>
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GET /api/v1/businesses/{business_id}/users/{user_id}/faces<br>
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DELETE /api/v1/businesses/{business_id}/users/{user_id}/faces/{face_id}<br>
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**Data Entities:**<br>
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**Cameras**<br>
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Camera Locations<br>
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**4.5 Camera Streamer Service**<br>
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**Responsibilities:**<br>
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Connect to IP cameras via RTSP/RTMP<br>
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Normalize video streams<br>
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Extract frames at configurable intervals<br>
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Preprocess frames for face detection<br>
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Push frames to message queue<br>
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Handle stream reconnection<br>
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Monitor stream health<br>
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Technology Stack:<br>
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FFmpeg for stream processing<br>
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OpenCV for frame extraction<br>
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GStreamer (alternative)<br>
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Configuration:<br>
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Frame extraction rate: 2-5 FPS<br>
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Image resolution: 640x480 or higher<br>
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Supported protocols: RTSP, RTMP, HTTP<br>
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Reconnection strategy: Exponential backoff<br>
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Processing Flow:<br>
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**4.6 Face Recognition Engine (Core AI Service)**<br>
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POST /api/v1/businesses/{business_id}/cameras<br>
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GET /api/v1/businesses/{business_id}/cameras<br>
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GET /api/v1/businesses/{business_id}/cameras/{camera_id}<br>
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PUT /api/v1/businesses/{business_id}/cameras/{camera_id}<br>
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DELETE /api/v1/businesses/{business_id}/cameras/{camera_id}<br>
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POST /api/v1/businesses/{business_id}/cameras/{camera_id}/heartbeat<br>
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GET /api/v1/businesses/{business_id}/cameras/{camera_id}/status<br>
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Camera Stream → Stream Reader → Frame Extractor →<br>
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Image Preprocessor → Message Queue (frame_ready)<br>
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**Responsibilities:**<br>
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Face detection in frames<br>
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Face recognition and matching<br>
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Confidence score calculation<br>
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Face encoding generation<br>
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Match user against database<br>
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Generate recognition events<br><br>
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Handle multiple faces in frame<br>
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Unknown person detection<br>
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Technology Stack:<br>
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Face Detection: MTCNN / Haar Cascade / YOLO<br>
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Face Recognition: FaceNet / DeepFace / ArcFace<br>
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Framework: TensorFlow / PyTorch<br>
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Face Encoding: 128/512-dimensional vectors<br>
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Processing Pipeline:<br>
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Matching Algorithm:<br>
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Input Frame → Face Detection → Face Alignment →<br>
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Feature Extraction → Encoding Generation →<br>
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Database Matching → Event Generation<br>
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python<br>
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Performance Optimization:<br>
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Use Redis cache for frequently accessed face encodings<br>
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Batch processing for multiple faces<br>
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GPU acceleration for encoding generation<br>
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Horizontal scaling with load balancing<br>
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**4.7 Webhook Dispatcher Service**<br>
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**Responsibilities:**<br>
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Send recognition events to business webhooks<br>
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Retry failed deliveries<br>
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Sign webhook payloads<br>
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Track delivery status<br>
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Handle timeout scenarios<br>
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Queue management
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Delivery Strategy:
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def match_face(face_encoding, business_id):<br>
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**json:**
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```json
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{
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"event_id": "uuid",
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"event_type": "RECOGNIZED",
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"business_id": "uuid",
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"timestamp": "2025-10-09T10:30:00Z",
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"camera": {
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"camera_id": "uuid",
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"camera_name": "Entrance Camera 01",
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"location": "Main Entrance"
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},
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"user": {
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"user_id": "uuid",
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"name": "John Doe",
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"employee_id": "EMP001"
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},
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"recognition": {
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"confidence_score": 0.94,
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"image_url": "https://storage.example.com/events/12345.jpg"
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}
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```
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**API Response Format**<br>
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**Success Response:**<br>
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**json:**
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```json
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{
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"success": true,
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"data": {
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"user_id": "uuid",
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"first_name": "John",
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"last_name": "Doe",
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"status": "ACTIVE"
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},
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"message": "User created successfully",
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"timestamp": "2025-10-09T10:30:00Z"
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}
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```
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**Error Response:**<br>
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```json
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{
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"success": false,
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"error": {
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"code": "VALIDATION_ERROR",
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"message": "Invalid input data",
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"details": [
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{
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"field": "email",
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"message": "Invalid email format"
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}
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]
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},
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"timestamp": "2025-10-09T10:30:00Z"
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}
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```
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**Pagination Response:**<br>
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```json
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{
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"success": true,
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"data": [...],
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"pagination": {
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"page": 1,
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"limit": 50,
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"total_records": 150,
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"total_pages": 3
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},
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"timestamp": "2025-10-09T10:30:00Z"
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}
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```