Comparison of Neural Network Algorithms for Face Recognition

In the last couple of decades, engineers, neuroscientists and psychologists have turned their attention to face recognition by humans and computer vision systems. Images of different complexities have been tested with a variety of methods. The goals of each research vary, as vary the applications. We present a neural method of recognizing faces using features obtained from compression of these faces with different methods. The extracted fea ti tres are used as inputs to a feedforward neural network. The neural network is trained with backpropagation and ALOPEX. Different types of featicre extraction are used and the results of training and testing for recognition based on the above mentioned methods are compared. ALOPEX converges much faster than backpropagation to a global maximum. Testing in both methods is as good as the learning of the network.

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