Higher Order Neural Network Group-based Adaptive Tolerance Trees

Higher Order Neural Network Group-based Adaptive Tolerance Trees

Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-61520-711-4.ch001
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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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Artificial higher order neural network models have been widely used for patten recognition with the benefit of HONNs being open box models (Bishop (1995); Park, Smith, & Mersereau (2000); Spirkovska, & Reid (1992, and1994); and Zhang, Xu, & Fulcher (2007)). Shin and Ghosh (1991) introduce a novel feedforward network called the pi-sigma network. This network utilizes product cells as the output units to indirectly incorporate the capabilities of higher order networks while using a fewer number of weights and processing units. The pi-sigma network is an efficient higher order neural network for pattern classification and function approximation. Linhart and Dorffner (1992) present a self-learning visual pattern explorer and recognizer using a higher order neural network, which could improve the efficiency of higher order neural networks, is built into a pattern recognition system that autonomously learns to categorize and recognize patterns independently of their position in an input image. Schmidt and Davis (1993) explore alternatives that reduce the number of network weights while pattern recognition properties of various feature spaces for higher order neural networks. Spirkovska and Reid (1993) describe coarse-coded higher order neural networks for PSRI object recognition. The authors describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Simulations show that a third-order neural network can be trained to distinguish between two objects of 4096×4096 pixels. Wan and Sun (1996) show that the higher order neural networks (HONN) have numerous advantages over other translational rotational scaling invariant (TRSI) pattern recognition techniques for automatic target recognition. Morad and Yuan (1998) present a method for automatic model building from multiple images of an object to be recognized. The model contains knowledge which has been computed during the learning phase from large 2D images of an object for automatic model building and 3D object recognition. A neuro-based adaptive higher order neural network model has been developed by Zhang, Xu, and Fulcher (2002) for data model recognition. Voutriaridis, Boutalis, and Mertzios (2003) propose ridge polynomial networks in pattern recognition. Ridge polynomial networks (RPNs) are special class of high order neural networks with the ability of high order neural networks for perform shift and rotation. Development of new higher order neural network models for face recognition is the first motivation of the chapter.

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