# CryptoFace: End-to-End Encrypted Face Recognition

## TL;DR

CryptoFace is a pioneering end-to-end encrypted face recognition system that leverages fully homomorphic encryption to securely process facial data throughout all stages, achieving high accuracy and efficiency.

## Contribution

It introduces a novel FHE-based face recognition pipeline with shallow patch convolutional networks, enabling secure, high-accuracy, and low-latency face recognition.

## Key findings

- Significantly accelerates inference compared to previous FHE methods.
- Increases verification accuracy on standard benchmarks.
- Supports secure face recognition without exposing raw data.

## Abstract

Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and, thus, inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at https://github.com/human-analysis/CryptoFace.