Biometric authentication via complex oculomotor behavior

This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in complex oculomotor behavior to distinguish individuals. Considered features include: saccadic dysmetria, compound saccades, dynamic overshoot, and express saccades. Score-level information fusion is applied and evaluated by: likelihood ratio, support vector machine, and random forest. The results suggest that it is possible to obtain equal error rates of 25% and rank-1 identification rates of 47% using score-level fusion by likelihood ratio.