Thomas Kübler

Nora Castner, Thomas Kübler, Katharina Scheiter et al.

Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks

Thiago Santini, Wolfgang Fuhl, Thomas Kübler et al.

Smooth pursuit eye movements provide meaningful insights and information on subject's behavior and health and may, in particular situations, disturb the performance of typical fixation/saccade classification algorithms. Thus, an automatic and efficient algorithm to identify these eye movements is paramount for eye-tracking research involving dynamic stimuli. In this paper, we propose the Bayesian Decision Theory Identification (I-BDT) algorithm, a novel algorithm for ternary classification of eye movements that is able to reliably separate fixations, saccades, and smooth pursuits in an online fashion, even for low-resolution eye trackers. The proposed algorithm is evaluated on four datasets with distinct mixtures of eye movements, including fixations, saccades, as well as straight and circular smooth pursuits; data was collected with a sample rate of 30 Hz from six subjects, totaling 24 evaluation datasets. The algorithm exhibits high and consistent performance across all datasets and movements relative to a manual annotation by a domain expert (recall: μ= 91.42%, σ= 9.52%; precision: μ= 95.60%, σ= 5.29%; specificity μ= 95.41%, σ= 7.02%) and displays a significant improvement when compared to I-VDT, an state-of-the-art algorithm (recall: μ= 87.67%, σ= 14.73%; precision: μ= 89.57%, σ= 8.05%; specificity μ= 92.10%, σ= 11.21%). For algorithm implementation and annotated datasets, please contact the first author.