Abstract
We study the feasibility of Fully connected Neural Networks (FNN) fed by principal components and Convolutional Neural Networks (CNN) on a face recognition benchmark in the case of heavy disguise measures. We observed that in certain cases, the local features makes the CNN performance biased. Having introduced an ensemble of neural networks, we observed that global features fed in FNN create different bias. The CNN and FNN bias differentiation can be used for covering the existing gaps in the disguised face recognition.
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Selitskiy, S., Christou, N., Selitskaya, N. (2021). Benchmarking of Fully Connected and Convolutional Neural Networks on Face Recognition: Case of Makeup and Occlusions. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_20
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