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Andrew Trapp - Using Density To Identify Fixations In Gaze Data Optimization-Based Formulations And Algorithms

Machine Learning and Friends Lunch
February 23
Computer Science Building, Room 150/151



Worcester Polythechnic Institute

Using Density To Identify Fixations In Gaze Data Optimization-Based Formulations And Algorithms
 

Abstract:

Eye tracking is an increasingly common technology with a variety of practical uses. Eye-tracking gaze data can be categorized into two main events: fixations, which represent attention, whereas saccades occur between fixation events. We propose a novel manner to identify fixations based on their density, which concerns both the fixation duration as well as its inter-point proximity. We develop two mixed-integer nonlinear programming formulations and corresponding algorithms to recover the densest fixations in a data set. Our approach is parameterized by a unique value that controls for the degree of desired density. We conclude by discussing computational results and insights on real data sets.