face-recognition-system-builder

license	Apache-2.0
name	face-recognition-system-builder
description	Build face recognition systems with InsightFace, ArcFace, enrollment pipelines, HDBSCAN clustering, and privacy-compliant architecture. Activate on: face recognition, face enrollment, face clustering, identity verification, facial search. NOT for: general object detection (computer-vision-pipeline), emotion analysis (ai-engineer).
allowed-tools	Read,Write,Edit,Bash(python:*,pip:*,npm:*,npx:*)
category	AI & Machine Learning
tags	["face-recognition","insightface","arcface","biometrics","privacy"]
pairs-with	[{"skill":"computer-vision-pipeline","reason":"Face detection is a specialization of the general CV pipeline"},{"skill":"video-frame-extraction-analysis","reason":"Face recognition on video requires keyframe extraction first"},{"skill":"data-pipeline-engineer","reason":"Large-scale enrollment and batch processing need pipeline orchestration"}]

Face Recognition System Builder

Build production face recognition systems with InsightFace/ArcFace for enrollment, verification, identification, and clustering with privacy-compliant architecture.

Decision Points

Model Selection Decision Tree

Requirements Analysis:
├── Latency < 50ms per face?
│   ├── YES: Use buffalo_sc (lightweight, 0.7 accuracy)
│   └── NO: Continue to accuracy analysis
├── Accuracy > 95% required?
│   ├── YES: Use AdaFace (premium accuracy, 200ms latency)
│   └── NO: Use buffalo_l (balanced, 100ms latency)
└── Mobile/Edge deployment?
    ├── YES: buffalo_sc or custom quantized model
    └── NO: buffalo_l or AdaFace

Threshold Tuning Heuristic

Use Case	FAR Target	FRR Target	Threshold Range	Validation
Security/Access Control	<0.1%	1-5%	0.55-0.65	Test on impostor pairs
Photo Organization	2-5%	<1%	0.35-0.45	Test on family albums
Investigation/Forensics	<0.01%	10-20%	0.65-0.75	Test on mugshot datasets
Social Media Tagging	5-10%	<2%	0.30-0.40	Test on casual photos

Enrollment Quality Decision

Per photo assessment:
├── Face detected with confidence > 0.7?
│   ├── NO: Reject photo, request new one
│   └── YES: Continue
├── Face size > 80px AND < 50% of image?
│   ├── NO: Reject (too small or too close)
│   └── YES: Continue
├── Pose deviation < 30 degrees (yaw/pitch)?
│   ├── NO: Flag as "supplementary only"
│   └── YES: Mark as "primary reference"
└── Final decision:
    ├── 3+ primary references: Complete enrollment
    ├── 1-2 primary + 2+ supplementary: Proceed with warning
    └── <3 total valid: Require additional photos

Failure Modes

1. Pose/Lighting Degradation

Detection: Similarity scores dropping consistently below historical baseline (>10% decrease) Symptoms: Good enrollments start failing verification, complaints of "system doesn't recognize me" Diagnosis: Environmental conditions changed (new lighting, camera angle, seasonal changes) Fix:

• Re-tune threshold based on recent data
• Add current condition photos to existing enrollments
• Implement adaptive threshold based on lighting detection