Gaze Based Personal Identification

Abstract

This chapter describes the use of visual attention characteristics as a biometric for authentication or identification of individual viewers. The visual attention characteristics of a person can be easily monitored by tracking the gaze of a viewer during the presentation of a known or unknown visual scene. The positions and sequences of gaze locations during viewing may be determined by overt (conscious) or covert (subconscious) viewing behaviour. Methods to quantify the spatial and temporal patterns established by the viewer for both overt and covert behaviours are proposed. The former behaviour entails a simple PIN-like approach to develop an independent signature while the latter behaviour is captured through three proposed techniques: a principal component analysis technique (‘eigenGaze’); a linear discriminant analysis technique; and a fusion of distance measures. Experimental results suggest that both types of gaze behaviours can provide simple and effective biometrics for this application.

Remote Eye Gaze Tracking Research: A Comparative Evaluation on Past and Recent Progress

“…They broadly classified gaze application usage into two fields, diagnostic (see examples in [234][235][236]) and interactive (see examples in [237,238]). Later reviews in [63,91,239] presented wider usage for gaze applications, which included that by REGT systems, augmented reality systems [240][241][242][243][244], human behavior analysis [245][246][247][248][249][250], and bio-metric authentication [251][252][253][254][255]. Recently, the usage of REGTs has gotten even wider with emerging usage in privacy-aware interactions.…”

Visual attention modelling for subjective image quality databases

“…For this reason, the mean μ(j) is computed for all GP in the current cluster including the new GP (i) at a particular time instance i. If the distance of GP (i) to the mean μ(j) is below Algorithm 1 Pseudo code for VFP creation [15].…”

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

“…Applying an information fusion method, they combined these features and reported a 27% error rate for personal identification. Fookes, Maeder, Sridharan, and Mamic (2009) developed methods to characterize visual attention as a biometric for authentication and identification of image viewers. To characterize spatial and temporal patterns of visual attention, they proposed three techniques: principal component analysis (''eigenGaze''), linear discriminant analysis, and a fusion of distance measures.…”