Task Classification during Visual Search with Deep Learning Neural Networks and Machine Learning Methods

TL;DR: In this paper , the authors used deep learning and SVM models on RGB images generated from fixation scan paths from visual attention tasks and used AdaBoost on filtered eye movement data as a baseline.

Abstract: Studies have shown the possibility to classify user tasks from eye-movement data. We present a new way to determine the optimal model for different visual attention tasks using data that includes two types of visual search tasks, a visual exploration task, a blank screen task, and a task where a user needs to fixate at the center of any scene. We used deep learning and SVM models on RGB images generated from fixation scan paths from these tasks. We also used AdaBoost on filtered eye movement data as a baseline. Our study shows that deep learning gives the best accuracy for classifying between visual search tasks but misclassified between visual search and visual exploration tasks. Machine learning-based methods performed with high accuracy classifying tasks that involve minimal visual attention. Our study gives insight on the best model to choose by type of visual task using eye movement data.