The most technically advanced face search tool available to the public. Our deep learning pipeline converts faces into 512-dimensional embeddings and searches them against our entire index using HNSW vector similarity — in under one second.
Search a Face Now — FreeProtevio uses a multi-stage pipeline built on state-of-the-art computer vision research. When you upload a photo, our system first detects all faces using a neural network trained on extensive datasets. Each detected face is processed through a deep embedding network that converts it into a 512-dimensional mathematical representation — essentially a unique facial fingerprint.
This embedding captures the essential geometry and features of the face in a way that is invariant to lighting, angle, expression, and even aging. The embedding is compared against our database using HNSW (Hierarchical Navigable Small World) vector indexing, enabling sub-second searches across our entire index.
Each face is encoded into a 512-dimensional vector capturing unique facial geometry, proportions, and features with mathematical precision.
Hierarchical Navigable Small World graphs enable approximate nearest neighbor search at blazing speeds — scanning the entire database in milliseconds.
Our crawlers discover and index thousands of new faces daily from across the open web. The database grows more comprehensive every day.
Personal identity protection: Discover where your face appears across the internet. Monitor for unauthorized use, deepfakes, or identity theft. Take action with built-in DMCA tools.
Photography and modeling: Track where portraits and professional photos are published. Verify that clients and agencies respect usage agreements. Find unauthorized reproductions.
Brand and reputation management: Ensure executives’ images are not associated with fraudulent businesses, fake reviews, or misleading content.
Research and verification: Cross-reference photos across sources and investigate visual misinformation. Protevio provides a powerful tool for visual fact-checking.
What happens behind the scenes when you search a face.
Our neural network (InsightFace RetinaFace) detects all faces in your photo, estimates landmarks, and aligns each face to a canonical 112×112 crop for optimal embedding quality.
Each aligned face passes through a deep ArcFace network (buffalo_l) that outputs a 512-dimensional L2-normalized vector — a mathematical fingerprint of the face’s unique geometry.
The embedding is compared against our PostgreSQL pgvector index using cosine similarity. HNSW graph traversal finds the top matches across the entire database in milliseconds.