Biometric Recognition via Probabilistic Spatial Projection of Eye Movement Trajectories in Dynamic Visual Environments

This paper proposes a method for the extraction of biometric features from the spatial patterns formed by eye movements during an inspection of dynamic visual stimulus. In the suggested framework, each eye movement signal is transformed into a time-constrained decomposition by using a probabilistic representation of spatial and temporal features related to eye fixations and called fixation density map (FDM). The results for a large collection of eye movements recorded from 200 individuals indicate the best equal error rate of 10.8% and Rank-1 identification rate as high as 51%, which is a significant improvement over existing eye movement-driven biometric methods. In addition, our experiments reveal that a person recognition approach based on the FDM performs well even in cases when eye movement data are captured at lower than optimum sampling frequencies. This property is very important for the future ocular biometric systems where existing iris recognition devices could be employed to combine eye movement traits with iris information for increased security and accuracy. Considering that commercial iris recognition devices are able to implement eye image sampling usually at a relatively low rate, the ability to perform eye movement-driven biometrics at such rates is of great significance.