Abstract
Face recognition is one of the biometric methods that is used to identify any given face image using the main features of this face. In this research, a face recognition system was suggested based on four Artificial Neural Network (ANN) models separately: feed forward backpropagation neural network (FFBPNN), cascade forward backpropagation neural network (CFBPNN), function fitting neural network (FitNet) and pattern recognition neural network (PatternNet). Each model was constructed separately with 7 layers (input layer, 5 hidden layers each with 15 hidden units and output layer). Six ANN training algorithms (TRAINLM, TRAINBFG, TRAINBR, TRAINCGF, TRAINGD, and TRAINGD) were used to train each model separately. Many experiments were conducted for each one of the four models based on 6 different training algorithms. The performance results of these models were compared according to mean square error and recognition rate to identify the best ANN model. The results showed that the PatternNet model was the best model used. Finally, comparisons between the used training algorithms were performed. Comparison results showed that TrainLM was the best training algorithm for the face recognition system.
Highlights
Human Face represents complex, multidimensional, meaningful visual motivation
Another Artificial Neural Network (ANN) is cascade forward backpropagation neural network (CFBPNN) and it is similar to feed forward backpropagation neural network (FFBPNN) but it includes a connection from input and every previous layer to following layers
Mean square error (MSE) is used as a performance function of the suggested face recognition system and it is minimized during ANN training
Summary
Human Face represents complex, multidimensional, meaningful visual motivation. It is difficult to develop a computational model for face recognition. ANN simulates the way neurons work in the human brain This is the main reason for its role in face recognition. The objective of this research is to develop a face recognition system based on using 4 different ANN models: feed forward Backpropagation neural network (FFBPNN), cascade forward Backpropagation neural network (CFBPNN), function fitting (FitNet), and pattern recognition (PatternNet). Each one of these models was constructed separately with 7 layers (input, 5 hidden layers and output layer) architectures. The research includes the following sections: Section II includes related literature; Section III includes details about ANN architectures and training algorithms; Section IV explains research methodology; Section V includes implementation steps of the face recognition system; Section VI includes the experimental results; and Section VII concludes this work
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