  We study involuntary micro-movements of the eye for biometric identification.
While prior studies extract lower-frequency macro-movements from the output of
video-based eye-tracking systems and engineer explicit features of these
macro-movements, we develop a deep convolutional architecture that processes
the raw eye-tracking signal. Compared to prior work, the network attains a
lower error rate by one order of magnitude and is faster by two orders of
magnitude: it identifies users accurately within seconds.
