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Higher Order Neural Network Group-based Adaptive Tolerance Trees
Abstract
Recent artificial higher order neural network research has focused on simple models, but such models have not been very successful in describing complex systems (such as face recognition). This chapter presents the artificial Higher Order Neural Network Group-based Adaptive Tolerance (HONNGAT) Tree model for translation-invariant face recognition. Moreover, face perception classification, detection of front faces with glasses and/or beards, and face recognition results using HONNGAT Trees are presented. When 10% random number noise is added, the accuracy of HONNGAT Tree for face recognition is 1% higher that artificial neural network Group-based Adaptive Tolerance (GAT) Tree, and is 6% higher than a general tree. When the gamma value of the Gaussian Noise exceeds 0.3, the accuracy of HONNGAT Tree for face recognition is 2% higher than GAT Tree, and is about 9% higher than that of a general tree. The artificial higher order neural network group-based adaptive tolerance tree model is an open box model and can be used to describe complex systems.
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