Improving performance in small datasets via pre-trained architectures based on VGGFace and VGGFace2 datasets

Resumo


Algoritmos de reconhecimento facial têm alcançado excelentes resultados sob condições controladas, principalmente por meio de técnicas computacionais de aprendizado profundo. No entanto, o desempenho em condições não controladas ainda precisa ser melhorado. Os sistemas de reconhecimento facial em problemas do mundo real geralmente lidam com condições não controladas, tais como, oclusões e variações de pose e iluminação, que degradam o desempenho do reconhecimento. Apesar dessas limitações, com amostras suficientes de treinamento, ainda é possível alcançar alto desempenho por meio das arquiteturas existentes de aprendizado profundo. No entanto, a falta de amostras de treinamento geralmente resulta em baixa precisão de reconhecimento nesse domínio. Neste estudo, foi demonstrado que a utilização de modelos pré-treinados para a tarefa de reconhecimento facial pode melhorar significativamente o desempenho em cenários com um baixo número de imagens de treinamento disponíveis.

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Publicado
25/09/2023
SIMÕES, Rodolfo; KEMMER, Bruno; IVAMOTO, Victor; LIMA, Clodoaldo. Improving performance in small datasets via pre-trained architectures based on VGGFace and VGGFace2 datasets. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 112-125. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.233822.