Integrating eye gaze into machine learning using fractal curves

Eye gaze tracking has traditionally employed a camera to capture a participant’s eye move- ments and characterise their visual fixations. However, gaze pattern recognition is still challenging. This is due to both gaze point sparsity, and a seemingly random approach participants take to viewing unfamiliar stimuli without a set task. Our paper proposes a method for integrating eye gaze into machine learning by con- verting a fixation’s two dimensional (x, y) coordinate into a one dimensional Hilbert curve distance metric, making it well suited for implementation into machine learning. We will compare this approach to a traditional grid-based string substitution technique, with an example implementation demonstrated in a Support Vector Machine and Convolutional Neural Network. Finally, a comparison will be made to examine what method performs better. Results have shown that this method can be both useful to dynamically quantise scan- paths for tuning statistical significance in large datasets, and to investigate the nuances of similarity found in shared bottom-up processing when participants observe unfamiliar stimuli in a free viewing experiment. Real world applications can include expertise-related eye gaze prediction, medical screening, and image saliency identification. Keywords: Neuroscience, eye tracking, fractals, support vector machine, convolutional neural network.