DOI: 10.1109/ACCESS.2019.2921914
Abstract: Any viable algorithm to infer affective states of individuals with autism requires natural and reliable data in real time and in an uncontrolled environment. For this purpose, this study provides a new natural-spontaneous affective-cognitive dataset based on facial expressions, eye gaze, and head movements for adult students with and without Asperger syndrome (AS). The data gathering and collecting in a computer-based learning environment is one of the significant areas, which has attracted researchers’ attention in affective computing applications. Due to the important impact of emotions on students learning outcome and their performance, the dataset included a range of affective-cognitive states which goes beyond basic emotions. This study reports the methodology that was used in data collection and annotation. Description and comparison of other available datasets were summarized, also the study presents the results that were concluded in more details. In addition, some challenges that were inherent to this study.
keywords: {Autism;Head;Visualization;Lighting;Videos;Real-time systems;Data collection;emotional dataset;autism;Asperger Syndrome;spontaneous;natural;facial expressions;affective computing;affective-cognitive states},
To read the paper go to URL: https://ieeexplore.ieee.org/document/8733795
All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon
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