  A plethora of research in the literature shows how human eye fixation pattern
varies depending on different factors, including genetics, age, social
functioning, cognitive functioning, and so on. Analysis of these variations in
visual attention has already elicited two potential research avenues: 1)
determining the physiological or psychological state of the subject and 2)
predicting the tasks associated with the act of viewing from the recorded
eye-fixation data. To this end, this paper proposes a visual saliency based
novel feature extraction method for automatic and quantitative classification
of eye-tracking data, which is applicable to both of the research directions.
Instead of directly extracting features from the fixation data, this method
employs several well-known computational models of visual attention to predict
eye fixation locations as saliency maps. Comparing the saliency amplitudes,
similarity and dissimilarity of saliency maps with the corresponding eye
fixations maps gives an extra dimension of information which is effectively
utilized to generate discriminative features to classify the eye-tracking data.
Extensive experimentation using Saliency4ASD, Age Prediction, and Visual
Perceptual Task dataset show that our saliency-based feature can achieve
superior performance, outperforming the previous state-of-the-art methods by a
considerable margin. Moreover, unlike the existing application-specific
solutions, our method demonstrates performance improvement across three
distinct problems from the real-life domain: Autism Spectrum Disorder
screening, toddler age prediction, and human visual perceptual task
classification, providing a general paradigm that utilizes the
extra-information inherent in saliency maps for a more accurate classification.
