Biometric recognition via fixation density maps

This work introduces and evaluates a novel eye movement-driven biometric approach that employs eye fixation density maps for person identification. The proposed feature offers a dynamic representation of the biometric identity, storing rich information regarding the behavioral and physical eye movement characteristics of the individuals. The innate ability of fixation density maps to capture the spatial layout of the eye movements in conjunction with their probabilistic nature makes them a particularly suitable option as an eye movement biometrical trait in cases when free-viewing stimuli is presented. In order to demonstrate the effectiveness of the proposed approach, the method is evaluated on three different datasets containing a wide gamut of stimuli types, such as static images, video and text segments. The obtained results indicate a minimum EER (Equal Error Rate) of 18.3 %, revealing the perspectives on the utilization of fixation density maps as an enhancing biometrical cue during identification scenarios in dynamic visual environments.