System Architecture

Detailed breakdown of the modular architecture and system components

Secure
Fast
Modular
Scalable

Architecture Overview

High-level system design and core principles

Architecture Overview

A modular, secure, and real-time face recognition system with clear separation of concerns: core logic, UI, data, and application wiring. Components are independently testable and replaceable.

Modular Monolith
Clear module boundaries inside one deployable
Modules encapsulate configuration, cryptographic logging, gallery management, recognition utilities, and UI surfaces. Internal contracts make refactors low risk.
Security-by-Design
Encrypted audit trail and integrity checks
Every critical event is logged as encrypted JSON with integrity hashes for tamper detection and compliance audits.
Real-Time Pipeline
Low-latency streaming and inference loop
Web-native streaming for capture → detect → encode → match → annotate with sub-100 ms latency at 30 FPS in typical CPU configurations.
Replaceable Engines
Swap detection/embedding models without UI changes
Abstractions around detection and encoding allow switching between HOG/CNN/SCRFD and different embedding models (FaceNet/ArcFace) with minimal code changes.

Modular Components

Individual modules and their interactions

Modular Components

Each module has a single responsibility and communicates through narrow interfaces to minimize coupling and maximize testability.

core/config.py
Central configuration, env parsing, and parameter validation
  • Typed env schema
  • Safe defaults
  • Runtime guards
  • Paths/resolution
core/logging_crypto.py
Encrypted JSON logs with integrity verification
  • Fernet encryption
  • SHA-256 hash
  • Tamper detection
  • Audit rotation
core/gallery.py
Face encoding store, enrollment, and metadata
  • Pickle/JSON export
  • Deduplication
  • Bulk import/export
  • Versioned schema
core/face_utils.py
Detection, encoding, matching, and annotation utilities
  • Batch/frame pipeline
  • Quality heuristics
  • Distance thresholds
  • Overlay/labels
ui/gradio_ui.py
Live recognition, enrollment, gallery, compare, and settings tabs
  • WebRTC live tab
  • Upload compare
  • Settings controls
  • Extensible tabs
ui/person_search_ui.py
Image-based person search, quality analysis, and reports
  • Known/unknown faces
  • Near-threshold suggestions
  • JSON export
  • Heuristics

Code Structure

Project organization and file hierarchy

Code Structure

High-level project layout highlighting responsibilities and runtime artifacts. Mirrors the research pipeline for clarity and maintainability.

Repository Layout
Folders, key files, and purpose notes
face-recognition-pipeline
core
config.py— Config & validation
logging_crypto.py— Encrypted logging + integrity
gallery.py— Encodings & metadata
face_utils.py— Detect/encode/match/annotate
ui
gradio_ui.py— Live UI with WebRTC
person_search_ui.py— Upload-based search
app.py— Entry point & runtime
requirements.txt— Dependencies
data
encodings
images
logs
CLI entry via app.py
Data folder excluded from VCS as needed
UI communicates via local interfaces

Quick Actions

1000+

Active Users

99.9%

Uptime

50ms

Response Time

1TB+

Data Processed