3-D Face Recognition System using Cylindrical-hidden Layer Neural Networks and its Optimization through Genetic Algorithms

B. Kusumoputro (Indonesia)


Face recognition system, cylindrical hidden layer, neural networks, backpropagation. Karhunen Loeve Transformation


In this paper, a 3-D face recognition system is developed using a cylindrical structure of hidden layer neural network and its optimization through genetic algorithms. The cylindrical structure of hidden layer is constructed by substituting each of neuron in its hidden layer of conventional multilayer perceptron with a circular-structure of neurons. The neural system is then applied to recognize a real 3-D face image from a database that consists of 5 Indonesian persons. The images are taken under four different expressions such as neutral, smile, laugh and free expression. The 2-D images is taken from the human model by gradually changing visual points, which is done by successively varies the camera position from – 90 to +90 with an interval of 15 degree. The experimental result has shown that the average recognition rate of about 64% could be achieved when we used the image in its spatial domain and about 84% when the image data is transformed to its eigen domain. Optimization of the hidden neurons is accomplished using genetic algorithms, which reduced the active neurons up to about 63.7% while increasing the recognition rate into about 94% in average.

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