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


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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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: