A Dual-Stream Challenge-Response Protocol for Ocular Liveness Verification
2026-07-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionCryptography and Security
AI summaryⓘ
The authors address challenges in eye-based biometric systems that are tricked by fake videos or deepfake attacks. They propose combining two eye responses—eye movement and pupil reaction—to a changing light pattern in a way that makes it hard for fakes to copy both at the same time. Using computer simulations based on known human eye response times, they show this method can distinguish real eyes from fakes better than current methods. They note that testing with real people is needed before using this approach widely.
ocular biometricspresentation attack detectionpupillary light reflexgaze trackingdeepfakeschallenge-response authenticationstate-space modelsmooth pursuit eye movementMonte Carlo simulationspoofing defense
Authors
Ismail Kably
Abstract
Ocular biometric systems face sophisticated presentation attacks, including high-resolution video replays and real-time generative deepfakes, which easily bypass static liveness checks. Current Presentation Attack Detection (PAD) frameworks typically rely on isolated physiological metrics, such as gaze tracking or the Pupillary Light Reflex (PLR), which can be spoofed independently. This paper proposes a Spatio-Luminance Sensor Fusion protocol, which introduces a dual-stream challenge-response framework for ocular liveness verification by uniting these metrics into a simultaneous authentication challenge. By generating a randomized, time-varying visual stimulus that fluctuates in both spatial trajectory and luminance intensity, we construct a mathematically coupled state-space likelihood model, termed the Synchronization Matrix, to evaluate the continuous cross-correlation between the expected biological latencies of smooth pursuit tracking and pupillary constriction. Using Monte Carlo simulation grounded in literature-derived latency distributions, we demonstrate theoretical separability between genuine and simulated attack conditions, and show that a multi-round challenge design improves the detection of generative deepfakes when a non-zero rendering-latency gap exists. This work provides a simulation-supported theoretical framework for next-generation dynamic spoofing defense in ocular and iris biometrics; human-subject validation is identified as necessary future work before deployment claims can be made.