Apesar de apresentar resultados compatíveis ou mesmo superiores se comparados com outros trabalhos recentes no reconhecimento de expressões faciais, o método proposto tam- bém deve ser testado com outras bases de imagens e/ou vídeos. A utilização de outras bases para treinamento e testes é importante não apenas para comprovar a eficiência do
método proposto, mas também para analisar o seu desempenho em bases com característi- cas diferentes de iluminação, posição da câmera, expressões faciais, oclusões parciais, etc.
A combinação do extrator da característica movimento apresentado neste trabalho com outros extratores de diferentes características (textura e forma, por exemplo) também é uma proposta de trabalho futuro. A combinação de métodos de extração de características é uti- lizada em outros tipos de aplicações, como detecção de pedestres [99], por exemplo. A escolha das características mais apropriadas para o reconhecimento de expressões continua sendo um problema desafiador, visto que a acuracidade do reconhecimento depende princi- palmente das características que são usadas para representar as expressões. De forma intui- tiva, algumas características parecem mais apropriadas do que outras para a representação das expressões. Contudo, ainda não há uma consenso na literatura sobre quais as melhores características a serem combinadas, tanto no reconhecimento de expressões faciais quanto em outras aplicações. A hipótese a ser provada é que a combinação de extratores de dife- rentes características pode prover taxas ainda melhores de reconhecimento das expressões faciais.
Finalmente, o uso do sistema proposto no reconhecimento de outros tipos de movimen- tos também pode ser alvo de estudos futuros. As atividades humanas, por exemplo, tam- bém são uma forma bastante efetiva de comunicação não-verbal. O reconhecimento des- sas atividades é o processo de corretamente identificar as ações realizadas pelo indivíduo. Existem várias aplicações nesta área, tais como: vídeos de vigilância, interação homem- máquina (HCI - Human-Computer Interaction), análises estatísticas em esportes, cuidados médicos, etc. Em vigilância, é usada para monitorar as atividades em casas inteligentes e também para detectar atividades anormais e alertar as autoridades competentes. Similar- mente, em HCI, esse tipo de reconhecimento fornece um método mais natural de interagir com o computador do que os convencionais mouse e teclado. Em sistemas de cuidados mé- dicos, as atividades dos pacientes podem ser monitoradas para facilitar uma recuperação mais rápida. Devido à tamanha variedade de aplicações, o reconhecimento de atividades humanas se tornou um tópico importante na comunidade científica, com muitas pesquisas sendo realizadas em todo o mundo [100].
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