Como trabalhos futuros, será importante buscar soluções para resolver de forma ainda mais efetiva os problemas gerados por movimentos abruptos e repetidos da cabeça e oclusões parciais da face.
Uma possível solução é treinar o CE-CLM (ZADEH et al., 2017) com imagens borradas,
geradas pelos movimentos bruscos. Visto que o OpenFace (BALTRUSAITIS et al., 2018)
disponibiliza código de treino, esta solução pode ser avaliada de forma rápida. Da mesma forma, o CE-CLM pode ser evoluído para lidar melhor com imagens de faces com oclusões parciais, geradas principalmente por acessórios. Por meio de treinamento com um maior número de imagens nesse contexto, o CE-CLM pode vir a ser capaz de tratar melhor estes cenários. Para alcançar um alinhamento facial mais preciso, também há a possibilidade de investigar o uso de um maior número de pontos de referência para realizar o casamento entre a face de entrada e as malhas 3D.
Também serão investigadas formas de conduzir avaliações adicionais através de um maior número de indivíduos e expressões faciais, usando bases de dados públicas, como o FaceWareHouse (CAO et al., 2013).
Outro ponto é o custo computacional. Serão investigadas formas de incrementar o desempenho do método proposto, o qual apresenta resultados mais lentos em comparação ao FaceCap original.
No contexto de utilização de recursos, também é importante investigar uma forma de executar o FaceCap (THOMAS; TANIGUCHI, 2016) usando apenas imagens RGB, elimi-
nando a necessidade da câmera de profundidade e propiciando um uso mais difundido da solução desenvolvida.
Por fim, é importante validar a solução proposta, e suas possíveis melhorias futuras, no desenvolvimento de aplicações de RA facial.
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