Biometric Recognition Methodology part 4/4 - Normalization

3.6 Normalization


Since the value of Backpropagation neural network required input range from zero (0) to one (1) as used the sigmoid activation function and it is found that most of the result produced in feature extraction using eigenfaces are not in particular range, thus the normalization process are required. In these proposed neural network, three types of different normalizations techniques had been selected for this reason [Puteh Saad, 2001]. They are the simple unit range (SUR), improve unit range (IUR) and improved linear scaling (ILS). Equation (3.42), (3.43) and (3.44) shows the computation needed for SUR, IUR and ILS respectively. The best technique adopted is based on the highest classification rate produced by backpropagation neural network.

With reference to the above equation  refers to the new value of feature,  in each dimension after the normalization process. Furthermore xmax and xmin refer to the maximum and minimum features value respectively. For the ILS computation,  refers to the mean of the feature and  is used for the standard deviation of the features in the same dimension. The dimensions for each vector are defined as .