Multidimensional wavelet neural networks Based on polynomial powers of sigmoid: a framework to image verification

DATJournal

Endereço:
Avenida Roque Petroni Júnior - 630 - Jardim das Acácias
São Paulo / SP
04707-000
Site: https://datjournal.anhembi.br/dat
Telefone: (11) 5095-5634
ISSN: 2526-1789
Editor Chefe: Gilbertto Prado
Início Publicação: 11/10/2016
Periodicidade: Semestral
Área de Estudo: Arquitetura e urbanismo

Multidimensional wavelet neural networks Based on polynomial powers of sigmoid: a framework to image verification

Ano: 2016 | Volume: 1 | Número: 2
Autores: J. F. Marar, A. Bordin
Autor Correspondente: J. F. Marar | [email protected]

Palavras-chave: Artificial neural network, Human face verification, Image processing, Pattern recognition, Polynomial powers of Sigmoid (PPS), Wavelets

Resumos Cadastrados

Resumo Inglês:

Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.