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Instant access to personal data is a double-edged sword and it has transformed society. It enhances convenience and interpersonal interactions through social media, while also making us all more vulnerable to identity theft and cybercrime. The need for hack-resistant biometric authentication is greater than ever. Previous studies have demonstrated that eye movements differ between individuals, so the characterization eye movements might provide a highly secure and convenient approach to personal identification, because eye movements are generated by the owner’s living brain in real-time and are therefore extremely difficult to imitate by hackers. To study the potential of eye movements as a biometric tool, we characterized the eye movements of 18 participants. We examined an entire battery of oculomotor behaviors, including the unconscious eye movements that occur during ocular fixation; this resulted in a high precision oculomotor signature that can identify individuals. We show that one-versus-one machine learning classification, applied with a nearest neighbor statistic, yielded an accuracy of >99% based with ~25minute sessions, during which participants executed fixations, visual pursuits, free viewing of images, etc. Even if we just examine the ~3 minutes in which participants executed the fixation task by itself, discrimination accuracy was higher than 96%. When we further split the fixation data randomly into 30 sec chunks, we obtained a remarkably high accuracy of 92%. Because eye-trackers provide improved spatial and temporal resolution with each new generation, we expect that both accuracy and the minimum sample duration necessary for reliable oculomotor biometric verification can be further optimized.