Redes neurais profundas para reconhecimento facial no contexto de segurança pública
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2020-07-29
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Universidade Federal de Goiás
Resumo
Face recognition is an important tool for law enforcement. Bein able to
compare a face image of a suspect filmed at a crime scene with a database of
millions of photos and thus find his true identity represents a significant
increase in crime resolution rates. Although this task has been researched
since the 1970s, it was with the use of Convolutional Neural Networks (RNCs)
from 2014 that a relevant advance was achieved that allowed some to reach
99.63% accuracy in the benchmark Labeled Faces in the Wild (LFW). Despite
different architectures and cost functions, a common feature of the papers
published since then is the fact that they are trained in a supervised manner,
thus requiring large collections of facial images previously labeled. Even state
of arts models in public benchmarks, they may not achieve the same results in
the real world. The main reason is the lack of demographic data distribuition of
these public datasets, which results in models with greater accuracy in specific
demographic subgroups and worst accuracy in other subgroups, such as afrodescendant
women. This work aims to investigate the fine tuning training
strategies of deep neural network architectures for facial recognition in public
safety context, using a dataset with the Brazilian faces in order to generate a
more accurate model for a investigations police department. We managed to
improve accuracy on test set with samples representative of the context of this
work training a model with private dataset with a very small number of
samples compared to the public ones.
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SILVA JÚNIOR, J. J. Redes neurais profundas para reconhecimento facial no contexto de segurança pública. 2020. 85 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2020.