Towards task-independent person authentication using eye movement signals

Abstract

Figure 1: A Gaussian mixture model (GMM) with universal backround model (UBM).User-dependent models are adapted from the UBM and the recognition score is normalized using the UBM score. AbstractWe propose a person authentication system using eye movement signals. In security scenarios, eye-tracking has earlier been used for gaze-based password entry. A few authors have also used physical features of eye movement signals for authentication in a taskdependent scenario with matched training and test samples. We propose and implement a task-independent scenario whereby the training and test samples can be arbitrary. We use short-term eye gaze direction to construct feature vectors which are modeled using Gaussian mixtures. The results suggest that there are personspecific features in the eye movements that can be modeled in a task-independent manner. The range of possible applications extends beyond the security-type of authentication to proactive and user-convenience systems.

The Impact of Temporal Proximity between Samples on Eye Movement Biometric Identification

“…However, there are several interesting experiments with different scenarios, including faces observation [25] or text reading [11]. There is also an attempt to perform identification without any information about a stimulus [18].…”

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

“…Although they reported superior performance from keystroke-based biometric features, they also noted that eye movementbased features showed promising results, which warrant further investigation. Kinnunen, Sedlak, and Bednarik (2010) developed a task-dependent person identification system by applying Gaussian mixture models on feature vectors of short-term eye gaze direction. Their results suggested that there are task-dependent person-specific features in the eye movement, which may be useful in user authentication systems.…”

“…For eye tracking data, proposed approaches include frequency analysis using the fast Fourier transform (Kinnunen et al, 2010), statistical analysis of velocity (Silver & Biggs, 2006), and morphological analysis based on graph-based representations (Rigas, Economou, & Fotopoulos, 2012) to name a few. In our previous studies (Yoon et al, 2014(Yoon et al, , 2015, we employed HMMs, a probabilistic model with promising performance for classifying independent temporal sequences.…”

Biometric Authentication Using Mouseand Eye Movement Data

“…More recently research has been done on the use of eye movement dynamics for biometric authentication [5,19,25,26]. Eye movement dynamics are similar to mouse dynamics except they track eye movement on a screen rather than the mouse.…”