This content originally appeared on DEV Community and was authored by wintrover
🎯 Project Overview
I've been tasked with developing a facial recognition-based user authentication system for the company platform, working independently on this critical project. This goes beyond simple feature development, encompassing real-time processing, multi-environment adaptation, and web-based implementation across various technical challenges.
🚀 Phase 1: Core Technology Development & Performance Optimization (July 30)
Deepface + MediaPipe Hybrid Architecture Implementation
I developed a real-time facial recognition prototype based on Deepface's ArcFace model. However, initial testing revealed significant recognition rate drops in various environments (lighting, angles, glasses, etc.).
Solution: Applied MediaPipe 3D landmark technology to dramatically improve recognition rates across diverse conditions.
Performance Optimization Tasks
- Processing Speed: Enhanced to 5.5 FPS
- Memory Management: Resolved memory leaks that occurred during technology integration
- Real-time Processing: Implemented real-time face detection based on webcam streaming
🏗 Phase 2: KYC System Architecture Design & Requirements Analysis (July 31 - August 1)
Requirements Gathering & System Design
Through development process reporting, I gathered specific requirements for the KYC procedure:
- User Guidance: Real-time guidance like "Look left, look right"
- Data Storage: Multi-frame capture and video storage capabilities
- Precision Analysis: Similarity analysis for specific facial regions (eyes, nose, mouth) in addition to full face
Architecture Transition
Shifted from desktop environment to web browser-based webcam system implementation.
Technical Review & Collaboration Environment Setup
- Patent Analysis: Compared ETRI and Apple patents to evaluate technical utility
- Collaboration Environment: Established Docker and GitHub-based development environment sharing
- Version Control: Developed branching strategies for efficient collaboration
🌐 Phase 3: Web-based System Implementation & Technical Challenge Resolution (August 4-5)
Backend Technology Stack Migration
To enable webcam functionality in web environment, migrated backend from Python to Express.js.
Key Changes:
- Python Flask ? Node.js Express.js migration
- WebSocket implementation for real-time communication
- RESTful API design
MediaPipe Web Integration Problem Resolution
Encountered several technical issues during MediaPipe web integration:
3.1 CSP (Content Security Policy) Conflicts
// Problem: CDN resources blocked by CSP
// Solution: Updated helmet middleware configuration
app.use(helmet({
contentSecurityPolicy: {
directives: {
scriptSrc: ["'self", "https://cdn.jsdelivr.net"],
connectSrc: ["'self", "https://cdn.jsdelivr.net"],
workerSrc: ["'self", "blob:"]
}
}
}));
3.2 File Loading Timing Issues
- Resolved WASM file loading delays
- Implemented Promise-based initialization for async loading
- Optimized module dependency management
3.3 Landmark Data Processing Errors
// Problem: Landmark coordinates returned as normalized values
// Solution: Added logic for pixel coordinate conversion
drawLandmarkPoints() {
this.ctx.arc(
landmark.x * this.canvas.width,
landmark.y * this.canvas.height,
this.landmarkStyle.radius,
0,
2 * Math.PI
);
}
🔧 Phase 4: Debugging & Performance Optimization
Key Issues & Resolution Process
4.1 Yaw Angle Measurement Error
Problem: Yaw angle measured abnormally high (85 degrees) when facing forward
Root Cause: Default values (0,0) from generateDummyLandmarks function included in face width calculation
Solution: Modified calculateAngles function to skip dummy landmarks
// Exclude dummy landmarks during face width calculation
const validLandmarks = landmarks.filter(landmark =>
landmark.x !== 0 || landmark.y !== 0
);
const faceWidth = Math.max(...validLandmarks.map(l => l.x)) -
Math.min(...validLandmarks.map(l => l.x));
4.2 Landmark Rendering Performance Issues
Problem: Performance degradation when rendering 478 landmark points in real-time
Solutions:
- Removed 95% of unnecessary console logs
- Canvas rendering optimization
- Implemented smooth animations using requestAnimationFrame
4.3 Server Connection Issues
Problem: FaceRecognitionClient's testServerConnection method causing infinite loops
Solution: Added retry limits (20 attempts, ~2 seconds) and proper error handling for initialization failures
📊 Current Status & Remaining Tasks
✅ Completed Features
- Deepface + MediaPipe hybrid facial recognition system
- Web-based webcam integration
- Real-time landmark rendering (478 points)
- Basic KYC workflow implementation
- Performance optimization (5.5 FPS)
🔄 In Progress
- Yaw Angle Measurement Error: Currently debugging, need algorithm improvement for accurate angle measurement
- Quality Assessment System: Completing real-time quality evaluation functionality when server connected
- User Interface: Improving intuitive UI/UX for end users
🎯 Next Phase Plans
- Yaw Angle Measurement Accuracy Enhancement
- Multi-face Processing Feature Addition
- Liveness Detection Implementation for Security Enhancement
- Mobile Environment Support
- Production Server Deployment
💡 Key Learnings
Technical Growth
- Hybrid Architecture: Designed optimal solutions combining Deepface and MediaPipe strengths
- Web-based Real-time Processing: Gained experience implementing real-time facial recognition systems via webcam
- Performance Optimization: Enhanced skills in memory management and rendering optimization for large-scale data processing
Project Management
- Requirements Management: Realized the critical impact of requirements convergence in early design phases
- Technical Debt Management: Understood the importance of balancing performance and functionality
- Problem-solving Abilities: Improved systematic analysis and resolution of complex technical challenges
This project has been a significant growth opportunity, going beyond simple feature development to encompass real-time system design, performance optimization, and user experience improvement. I plan to continue developing and improving the system to create a more stable and accurate facial authentication solution.
This content originally appeared on DEV Community and was authored by wintrover
wintrover | Sciencx (2025-08-10T07:54:24+00:00) As a New Hire, I’m Leading the Company Platform’s Facial Authentication System Development. Retrieved from https://www.scien.cx/2025/08/10/as-a-new-hire-im-leading-the-company-platforms-facial-authentication-system-development/
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