A Semiparametric Model for Bayesian Reader Identification

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 585–594, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics A Semiparametric Model for Bayesian Reader Identification Ahmed Abdelwahab1 and Reinhold Kliegl2 and Niels Landwehr1 1 Department of Computer Science, Universit¨at Potsdam August-Bebel-Straße 89, 14482 Potsdam, Germany {ahmed.abdelwahab, niels.landwehr}@uni-potsdam.de 2 Department of Psychology, Universit¨at Potsdam Karl-Liebknecht-Straße 24/25, 14476 Potsdam OT/Golm kliegl@uni-potsdam.de Abstract We study the problem of identifying individu- als based on their characteristic gaze patterns during reading of arbitrary text. The motiva- tion for this problem is an unobtrusive biomet- ric setting in which a user is observed during access to a document, but no specific chal- lenge protocol requiring the user’s time and at- tention is carried out. Existing models of indi- vidual differences in gaze control during read- ing are either based on simple aggregate fea- tures of eye movements, or rely on paramet- ric density models to describe, for instance, saccade amplitudes or word fixation durations. We develop flexible semiparametric models of eye movements during reading in which den- sities are inferred under a Gaussian process prior centered at a parametric distribution fam- ily that is expected to approximate the true dis- tribution well. An empirical study on read- ing data from 251 individuals shows signifi- cant improvements over the state of the art. 1 Introduction Eye-movement patterns during skilled reading con- sist of brief fixations of individual words in a text that are interleaved with quick eye movements called saccades that change the point of fixation to another word. Eye movements are driven both by low-level visual cues and high-level linguistic and cognitive processes related to text understanding; as a reflection of the interplay between vision, cog- nition, and motor control during reading they are frequently studied in cognitive psychology (Kliegl et al., 2006; Rayner, 1998). Computational mod- els (Engbert et al., 2005; Reichle et al., 1998) as well as models based on machine learning (Matties and Søgaard, 2013; Hara et al., 2012) have been devel- oped to study how gaze patterns arise based on text content and structure, facilitating the understanding of human reading processes. A central observation in these and earlier psycho- logical studies (Huey, 1908; Dixon, 1951) is that eye movement patterns strongly differ between individu- als. Holland et al. (2012) and Landwehr et al. (2014) have developed models of individual differences in eye movement patterns during reading, and studied these models in a biometric problem setting where an individual has to be identified based on observing her eye movement patterns while reading arbitrary text. Using eye movements during reading as a bio- metric feature has the advantage that it suffices to observe a user during a routine access to a device or document, without requiring the user to react to a specific challenge protocol. If the observed eye movement sequence is unlikely to be generated by an authorized individual, access can be terminated or an additional verification requested. This is in con- trast to approaches where biometric …

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