# Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye

## TL;DR

This paper introduces a deep learning approach for biometric identification using involuntary eye micro-movements, achieving significantly lower error rates and faster identification times than previous macro-movement methods.

## Contribution

The study develops a deep convolutional neural network that processes raw eye-tracking signals for biometric identification, outperforming prior macro-movement based techniques.

## Key findings

- Lower error rate by one order of magnitude
- Faster identification by two orders of magnitude
- Accurate user identification within seconds

## Abstract

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-tracking signal. Compared to prior work, the network attains a lower error rate by one order of magnitude and is faster by two orders of magnitude: it identifies users accurately within seconds.