Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
Autism Spectrum Disorder (ASD) is a neuro-developmental disability marked by deficits in communicating and interacting with others. The standard protocol for diagnosis is based on fulfillment of a descriptive criteria, which does not establish precise measures and influence the late diagnosis. Th...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/49957 |
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| Resumo: | Autism Spectrum Disorder (ASD) is a neuro-developmental disability marked
by deficits in communicating and interacting with others. The standard protocol
for diagnosis is based on fulfillment of a descriptive criteria, which does not
establish precise measures and influence the late diagnosis. Thus, new diagnostic
approaches should be explored in order to better standardise practices. The
best case scenario would be to have a reliable automated system that indicates
the diagnosis with an acceptable level of assurance. At the moment, there
are no publicly available representative open-source datasets with the main
aim of this diagnosis. This work proposes a new methodology for collecting a
Face Biometrics dataset with the aim to investigate the differences in facial
expressions of ASD and Typical Developmental (TD) people. Thus, a new
dataset of facial images was collected from YouTube videos, and computer
vision-based techniques were used to extract image frames and filter the dataset.
We have also performed initial experiments using classical supervised learning
models as well as ensembles and managed to archive promising results. |
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