Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

A framework to image verification

  • João Fernando Marar
  • Aron Bordin
Keywords: Artificial neural network, Human face verification, mage processing, Pattern recognition, Polynomial powers of Sigmoid (PPS), Wavelets

Abstract

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.

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Published
2016-12-27
How to Cite
Marar, J. F., & Bordin, A. (2016). Multidimensional wavelet neural networks Based on polynomial powers of sigmoid: A framework to image verification. DAT Journal, 1(2), 106-123. https://doi.org/10.29147/2526-1789.DAT.2016v1i2p106-123