Authors
Abstract
In this paper, a recognition system for image identification by using
principal component analysis (PCA) and back propagation (BP) Neural Network is proposed. The system consists of three steps. At the very outset some preprocessing are applied on the input image. Secondly image features are extracted by using PCA, which will be taken as the input to the Back-propagation Neural Network (BPN) in the third step and classification. Principal Component Analysis (PCA) is one of the most popular appearance-based methods used mainly for dimensionality reduction in compression and recognition problems, this will reduce
the size of training data which it entered to neural network. In our work, The proposed model is tested on a number of images with different value of learning rate. Experimental results demonstrate the proposed model is better, efficient and it reduces the ratio of the number of iteration training to half comparing with results of the Neural Network
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