Evaluating the number of hidden neurons and hidden layers necessary for solving of face recognition, pattern recognition and classification tasks is one of the key problems in artificial neural networks. In this note, we show that artificial neural network with a two hidden layer feed forward neural network with d inputs, d neurons in the first hidden layer, 2d+2 neurons in the second hidden layer, k outputs and with a sigmoidal infinitely differentiable function can solve face recognition tasks. This result can be applied to design pattern recognition and classification models with optimal structure in the number of hidden neurons and hidden layers. In addition, we propose a new type of convolutional neural network, which is capable to extract most powerful features. The experimental results over well-known benchmark datasets shows that the convergence and the accuracy of the proposed model of artificial neural network is acceptable. Findings in this paper are experimentally analyzed on five different face datasets from machine learning repository.
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"Choosing the structure of convolutional neural networks for face recognition,"
Bulletin of National University of Uzbekistan: Mathematics and Natural Sciences: Vol. 3:
1, Article 2.