Identification of fixations,saccades and smooth pursuits based on segmentation and clustering

Abstract

Accurate automated eye movement classification is a key technique in the field of eye tracking, and the identification effects are largely influenced by the noise and imprecision of an eye tracker, and the characteristics of eye movements. In this paper, we propose a novel segmentation and clustering-based identification algorithm (I-SC) to effectively recognize fixations, saccades and smooth pursuits in eye tracking. In the proposed algorithm, firstly we employ the velocity feature in the recorded eye data to identify the saccade segments, and then the standard deviation of the dispersion is used to divide the remaining data into segments. Finally, in each segment we define the average direct distance feature and adopt the method of clustering by fast search and find of density peaks (CFSFDP) to classify fixations and smooth pursuits. To demonstrate its effectiveness and robustness, the proposed I-SC algorithm is evaluated with the eye tracking dataset sampled from 11 participants by a commercial eye tracker. The experimental results show that the proposed mechanism can achieve up to an accuracy of 96.0% and a recall of 87.6%, which is a considerably better performance than both the Velocity and Dispersion Threshold Identification (I-VDT) algorithm and the Convolutional Neural Networks (CNN) algorithm. With our mechanism, accurate classification can be achieved even with the noise and imprecision of data from eye trackers.