FaceID Recognition

Where It's Applied

In my practice, I apply facial recognition systems for access control, identification, and attendance analytics. The system operates at company events for rapid participant verification at entry, in security systems for detecting unauthorized access, at large events for counting people in venues and analyzing attendance flows. The system can work online, processing video streams from multiple cameras simultaneously and delivering real-time results.

Who Will Benefit

I recommend this solution to large companies hosting events with many participants needing automated entry control without human intervention. Security companies and surveillance systems for enhancing facility security and quickly detecting potential threats. Shopping centers, stadiums, and other mass gathering venues for attendance flow analytics and headcount. Corporate offices for access control to spaces and automatic employee attendance tracking.

Technologies

InsightFace as Core Recognition Engine

I use InsightFace — a powerful open-source facial recognition library developed by researchers at Megvii. It's not just a detector — it's a complete stack: face detection in images, face alignment, facial feature extraction (face embedding), and face comparison. InsightFace provides several pre-trained models of varying sizes and accuracy — from lightweight mobile models to high-precision models for critical tasks. In practice, recognition accuracy reaches 99%+ on quality photos.

Critical point: InsightFace handles faces at various positions and angles, manages partial face occlusion, glasses, and masks well (important at events).

Face Database and Encoding Management

I organize face storage as vector representations (embeddings). When someone registers for an event or is added to the system, their photo is processed through InsightFace and converted to a 512-dimensional feature vector. These vectors are stored in a fast database (I use PostgreSQL with pgvector extension for vector search or specialized stores like Faiss/Hnswlib). This enables finding matches in milliseconds even with hundreds of thousands of faces in the database.

The face database is built once (e.g., uploading participant lists and their photos) then used repeatedly throughout the event.

Mobile Collection System Without Installation

I developed a mobile web application (Progressive Web App) in Vue.js requiring no installation — just open a link in phone browser. The app uses device camera via WebRTC to capture video stream, processes frames locally (using ONNX Runtime or TensorFlow.js for InsightFace inference directly in browser) and sends results to server. This enables setting up mobile entry control points in minutes — just needs a QR code link.

Advantage: no installation, cross-platform (Android, iOS, any browser), can work with or without internet (offline-first architecture).

Real-Time Video Stream Processing from Multiple Cameras

The system supports simultaneous connection of multiple IP or USB cameras via RTSP/HTTP streams. I use OpenCV for video capture and decoding, with each frame processing asynchronously in separate threads or processes. On a laptop with NVIDIA GPU (CUDA), each stream can process 15-30 frames per second depending on resolution and InsightFace model. This means one machine can simultaneously handle 3-5 Full HD video streams.

From practical experience: 4000 event participants meant 2-3 hours of system operation with peak load at start (when everyone enters simultaneously). The system handled it on a laptop with RTX 3070 — processing each person in 50-100ms, fast enough for smooth checkpoint passage.

Lighting and Low-Light Operation

I integrated infrared lighting control for security operations and low-light conditions. The system can manage RGB and IR lighting via GPIO or relays, automatically enabling it at low illumination. InsightFace works well with infrared images (face visible by thermal signature), enabling operation in complete darkness. In practice, IR mode recognition accuracy is slightly lower but still acceptable (93-96%).

People Counting and Analytics

In addition to facial recognition, I implemented human figure detection for room occupancy counting. The system tracks unique faces over time and counts people in the venue. This provides clients real-time venue occupancy information, useful for fire safety compliance and people flow management. I also log all entries-exits, enabling attendance graph construction and analysis of various event areas' popularity.

GPU-Accelerated Architecture with CUDA

The entire system is developed using CUDA for maximum performance. InsightFace can run on CPU or GPU — I choose GPU version for high-load scenarios. Stack: Python + PyTorch for model inference, OpenCV for video capture, FastAPI for API server. All heavy computation (face detection, feature extraction, vector comparison) runs on GPU, providing 5-10x speedup versus CPU.

On laptop with NVIDIA RTX 3070, typical numbers: Full HD video face detection — 20-30ms, feature extraction — 15-25ms, search in 10,000 face database — 1-2ms. Total per person: 40-60ms processing, enabling real-time operation.

Queue Management and Flow Control

At high-traffic events, the system must handle queues. I use an optimistic approach: each video frame is analyzed, face added to search queue in database. If match found, system returns result (allow entry, show person information). If not found — person added to unknown list. Based on this, a decision is made: let them through or direct to operator for manual verification.

Synchronization and Data Consolidation

When operating multiple control points, the system synchronizes face database and results. I use PostgreSQL as central storage with local cache on each point (Redis). This enables system operation even with internet loss, with all data syncing upon reconnection. At a 4000-person event, each control point knew current information about who already passed to avoid duplicate counting.

Important Organizational Considerations

First and most critical — coordination with legal department and GDPR/local data protection law compliance. Processing biometric data (face is considered biometric data) requires explicit consent. At events, I always inform participants about facial recognition system presence and obtain consent during registration.

Second — photo quality in the face database critically affects accuracy. Must ensure good quality photos taken in good lighting with fully visible faces. In practice: poor photos cause false positives and misses. I always specify photo requirements during registration.

Third — error handling and false positives. Even at 99% accuracy with 4000 people there will be false matches. I organize fallback: if system is uncertain or finds multiple similar candidates, operator must manually confirm. This is critical for security.

Fourth — face database storage security. All vector representations and photos must be stored securely with access only to authorized staff. After the event, data should be either deleted or archived per company policy.

Fifth — performance monitoring. At large events, I track metrics: processing latency, percentage of successfully identified people, GPU load. Under overload, the system should gracefully degrade (e.g., reduce video resolution or frame processing frequency) rather than crash.