In January 2025, the FaceSearchAI team set an ambitious goal: identify 10,000 high-confidence lookalikes within 30 days while maintaining strict privacy standards. This article breaks down how we achieved it — from pipeline design to quality control and product improvements inspired by real data.
Project Overview
Find and verify 10,000 lookalike pairs at ≥ 0.6 similarity threshold.
Processed 12.7M indexed faces; average time to result: 7-10s.
Zero image retention by default; opt‑in anonymized metrics only.
Methodology
Face embeddings: CNN-based face encoders create 128‑d vectors per face.
Approximate nearest neighbors (ANN): Vector index for sub‑second candidate retrieval.
Re‑ranker: Secondary model refines candidates using multi‑view similarity.
Human‑in‑the‑loop QA: Blind audits on random samples to measure precision and drift.
Dataset & Ethics
Sources: Indexed public web images and user‑provided uploads with consent.
Compliance: Privacy‑by‑design, no storage of uploads by default, hashed telemetry only.
Bias checks: Periodic sampling across demographics to monitor false match rates.
Results
Challenges & Mitigations
- • Added pose‑aware augmentation for robustness
- • Weighted similarity across multi‑crop embeddings
- • Temporal ensembling of user images
- • Age‑invariant features via fine‑tuning
Product Improvements From the Sprint
Confidence labels: Clearer match confidence badges in results UI.
Batch uploads: Queueing with progress and per‑photo analytics.
Privacy toggles: One‑click data retention opt‑out made default.
Try Lookalike Search Yourself
Upload a clear, front‑facing photo to discover similar faces online. Works in seconds with privacy‑first processing.
Start Free Lookalike SearchClosing Thoughts
The 10,000‑lookalike sprint validated our indexing strategy, ANN search, and QA loop. Most importantly, it confirmed we can scale responsibly without compromising user privacy. Next up: even better performance on profiles, aging, and low‑light photos.