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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Autor principal: Budke, Jaine Rannow
Outros Autores: Abreu, Marjory Cristiany da Costa
Formato: Dissertação
Idioma:pt_BR
Publicado em: Universidade Federal do Rio Grande do Norte
Assuntos:
Computação
Análise facial
Transtorno do espectro autista
Ensemble
CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
Endereço do item:https://repositorio.ufrn.br/handle/123456789/49957
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spelling ri-123456789-499572022-12-01T23:34:30Z Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset Budke, Jaine Rannow Abreu, Marjory Cristiany da Costa http://lattes.cnpq.br/6545013954007575 https://orcid.org/0000-0001-7461-7570 http://lattes.cnpq.br/2234040548103596 Carvalho, Bruno Motta de Souza Neto, Plácido Antônio de https://orcid.org/0000-0003-1233-4510 http://lattes.cnpq.br/3641504724164977 Computação Análise facial Transtorno do espectro autista Ensemble CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO 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. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES O Transtorno do Espectro Autista (TEA) é um transtorno de neurodesenvolvimento marcado por déficits na comunicação e interação social. O protocolo padrão de diagnóstico é baseado no preenchimento de critérios descritivos por um profissional qualificado, o que não estabelece medidas precisas e influencia no diagnóstico tardio. Portanto, novas abordagens diagnósticas precisam ser exploradas para que haja uma melhor padronização das práticas clínicas. O melhor cenário seria a existência de um sistema automatizado e confiável que indicasse o diagnóstico com um nível de garantia satisfatório. Contudo, até o momento, não há bases de dados públicas e representativas com o objetivo de explorar diagnósticos alternativos. Esse trabalho investiga as diferenças nas expressões faciais de pessoas com TEA e Desenvolvimento Típico. Para isso, uma nova base de dados de imagens faciais foi coletada através de vídeos do YouTube e técnicas baseadas em visão computacional foram utilizadas para extrair frames dos vídeos, filtrar a base de dados e extrair características faciais das imagens. Também realizamos experimentos iniciais usando modelos clássicos de aprendizado supervisionado, bem como ensembles, e conseguimos atingir resultados promissores. 2022-12-01T23:25:08Z 2022-12-01T23:25:08Z 2022-09-16 masterThesis BUDKE, Jaine Rannow. Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset. Orientador: Márjory Cristiany da Costa Abreu. 2022. 106f. Dissertação (Mestrado em Sistemas e Computação) - Centro de Ciências Exatas e da Terra, Universidade Federal do Rio Grande do Norte, Natal, 2022. https://repositorio.ufrn.br/handle/123456789/49957 pt_BR Acesso Aberto application/pdf Universidade Federal do Rio Grande do Norte Brasil UFRN PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
institution Repositório Institucional
collection RI - UFRN
language pt_BR
topic Computação
Análise facial
Transtorno do espectro autista
Ensemble
CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
spellingShingle Computação
Análise facial
Transtorno do espectro autista
Ensemble
CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
Budke, Jaine Rannow
Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
description 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.
author2 Abreu, Marjory Cristiany da Costa
author_facet Abreu, Marjory Cristiany da Costa
Budke, Jaine Rannow
format masterThesis
author Budke, Jaine Rannow
author_sort Budke, Jaine Rannow
title Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
title_short Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
title_full Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
title_fullStr Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
title_full_unstemmed Face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
title_sort face biometrics for differentiating typical development and autism spectrum disorder: a methodology for collecting and evaluating a dataset
publisher Universidade Federal do Rio Grande do Norte
publishDate 2022
url https://repositorio.ufrn.br/handle/123456789/49957
work_keys_str_mv AT budkejainerannow facebiometricsfordifferentiatingtypicaldevelopmentandautismspectrumdisorderamethodologyforcollectingandevaluatingadataset
_version_ 1773963160057806848

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