Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye
This addresses the problem of secure and efficient user authentication for biometric systems, representing a strong incremental improvement over existing approaches.
The paper tackled biometric identification by using involuntary micro-movements of the eye, achieving a lower error rate by one order of magnitude and faster identification within seconds compared to prior methods.
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.